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Voluntary EMG-to-force estimation with a multi-scale physiological muscle model
Mitsuhiro Hayashibe1 &
David Guiraud1
EMG-to-force estimation based on muscle models, for voluntary contraction has many applications in human motion analysis. The so-called Hill model is recognized as a standard model for this practical use. However, it is a phenomenological model whereby muscle activation, force-length and force-velocity properties are considered independently. Perreault reported Hill modeling errors were large for different firing frequencies, level of activation and speed of contraction. It may be due to the lack of coupling between activation and force-velocity properties. In this paper, we discuss EMG-force estimation with a multi-scale physiology based model, which has a link to underlying crossbridge dynamics. Differently from the Hill model, the proposed method provides dual dynamics of recruitment and calcium activation.
The ankle torque was measured for the plantar flexion along with EMG measurements of the medial gastrocnemius (GAS) and soleus (SOL). In addition to Hill representation of the passive elements, three models of the contractile parts have been compared. Using common EMG signals during isometric contraction in four able-bodied subjects, torque was estimated by the linear Hill model, the nonlinear Hill model and the multi-scale physiological model that refers to Huxley theory. The comparison was made in normalized scale versus the case in maximum voluntary contraction.
The estimation results obtained with the multi-scale model showed the best performances both in fast-short and slow-long term contraction in randomized tests for all the four subjects. The RMS errors were improved with the nonlinear Hill model compared to linear Hill, however it showed limitations to account for the different speed of contractions. Average error was 16.9% with the linear Hill model, 9.3% with the modified Hill model. In contrast, the error in the multi-scale model was 6.1% while maintaining a uniform estimation performance in both fast and slow contractions schemes.
We introduced a novel approach that allows EMG-force estimation based on a multi-scale physiology model integrating Hill approach for the passive elements and microscopic cross-bridge representations for the contractile element. The experimental evaluation highlights estimation improvements especially a larger range of contraction conditions with integration of the neural activation frequency property and force-velocity relationship through cross-bridge dynamics consideration.
Any human movement is produced by muscular and skeletal systems controlled by the nervous system. Mechanism of the human body dynamics has been revealed by many biomechanical researches. The general neuromusculoskeletal model of the whole body and its dynamics computation package have been developed and reported[1]. However, the detailed neuromuscular activation system should still be closely analyzed and modeled from microscopic to macroscopic scales. Macroscopic modeling, such as the Hill-type muscle model, is a phenomenological model. Conversely, microscopic modeling is based on muscle physiology, more specifically the dynamics of actin-myosin, allowing it to display a richer response.
Neuromuscular modeling is important for neuroscience to understand how limb movements are smoothly and effectively controlled[2, 3]. It is also valuable for clinical applications to be used for quantitative analysis on abnormal muscle activation patterns such as spasticity induced by stroke or cerebral palsy[4]. Moreover, neuroprosthetic control such as functional electrical stimulation (FES) for paralyzed muscles[5, 6] can be improved through neuromuscular modeling to optimize the muscle activation control.
Surface electromyography (EMG) is widely used to assess the muscle status in a wide range of clinical fields. The signal generated during a contraction is the summation of the signals of all different recruited motor units (MU) at a given time. Each MU receives information from the central nervous system in the form of action potentials, which are transmitted from the alpha motor neuron to the muscle fibers via the neuromuscular junction. During voluntary contraction, the EMG signal can be seen as the interference pattern of all active MUs, with each MU firing at its own mean frequency with its own timing[7]. The contributions of individual MU cannot be easily recovered from the EMG signal. However, EMG signals still contain precious information about muscle activation and could thus represents the entry of a muscle model. Indeed, the important advantage of EMG usage is that it can account for a subject's individual activation pattern to estimate muscle force. Moreover, as the EMG signals can be considered for each individual groups of antagonist muscles with their own muscle models, co-contraction can be captured and deeply investigated through the computation of individual force and stiffness. On the contrary, numerical optimization techniques typically do not account for muscular co-contraction.
EMG-based models were already used in many studies to estimate torques around joints[8] or in musculoskeletal models[9, 10]. Most of the muscle models used in such studies are based on phenomenological models derived from Hill's study[11] and summarized by Zajac[12]. The Hill-type model is attractive in biomechanical simulations because of the computational simplicity and easy to calibrate using experimentally measurable variables. It tends to produce accurate estimation results, however in limited ranges of function.
However, recent work[13] performed the validation of Hill model under functionally wider relevant conditions. The authors concluded that the modeling errors were substantial for different firing frequencies and greatest at low motor unit firing rates that are most relevant in normal movement conditions both in voluntary and FES muscle contractions. They pointed out that this could be due to the Hill model assumption whereby muscle activation, force-length and force-velocity properties are considered independently. In their discussion, it was suggested that more physiological coupling between activation and force-velocity properties can be demonstrated in crossbridge models incorporating dependence between physiologically based activation and cross-bridge attachment[13]. The important finding is that Hill modeling errors were large for different firing frequencies and greatest at low motor unit firing rates. In[13], classical linear Hill model was used. Especially for solving the problem of the mismatch at low muscle activation level, a nonlinear conversion was proposed[14] and used by other researchers[9]. This can solve partially the problem, however, there is a still the problem to correspond to the estimations in different contraction speeds. Besides, in Hill-type modeling, the cut off frequency should be carefully chosen depending on the type of task because the envelope of the estimated force is almost defined by the low-pass filtering of the activation. Thus, we aim at proposing multiscale muscle model, which can have more physiological coupling between activation and force-velocity properties by incorporating microscopic cross-bride and dual dynamics pathway for the recruitment / activation level and the calcium triggering the contraction cycle. However, the whole passive structure is based on Hill model as only the contractile element differs.
The first microscopic muscle model was proposed by Huxley[15]. He detailed the interaction of the cross-bridges in a sarcomere in order to explain the force generation. The distinctions between microscopic and macroscopic levels are not absolute; thus a sarcomere model can be used to represent a whole muscle, which is assumed to be a homogeneous assembly of identical sarcomeres. Conversely, the Hill model, which was originally developed for whole muscles, has been used to represent the dynamics of individual sarcomeres within a fiber[16]. The distribution-moment approach of Zahalak[17] is a model for sarcomeres or whole muscle, which is extracted via a formal mathematical Gaussian approximation from Huxley cross-bridge model. This model fills a gap between microscopic and macroscopic levels. Based on Huxley and Hill-type models, Bestel-Sorine[18] proposed an explanation of how the beating of cardiac muscle may take place, through chemical control input connected to the calcium dynamics of cells in muscles. This chemical input triggers the contractile element model.
Complete Huxley type physiological model have a greater repertoire of response, but are computationally complex. Bestel-Sorine representation provides an efficient trade-off between complexity and power of the model expression. It consists in an explicit computation of the first two moments reprenting stiffness and force of the contractile element, triggered by the chemical input as regard the contraction start and stop. Starting with this concept, we first adapted it to the striated muscle model to represent muscle responses under FES[19]. The multi-scale muscle model was applied for voluntary contraction in a preliminary work[20]. In this study, we aim at developing EMG-based muscle model to study voluntary muscle contraction with this multiscale modeling approach compared to Hill approaches, both linear and nonlinear. Indeed, in Hill approach, the dependency on cut-off frequency choice prevents from good performances in a wide range of contraction types. The multiscale approach should improve the performances in particular regarding fast and slow contraction patterns. In addition, we consider that it is of interest to apply the same type of EMG-to-force estimation with a physiologically detailed model and not only with a phenomenological Hill model because the internal biophysical dynamics could shed new light on neuromuscular activation. Using common data sets of isometric muscle contractions, the force estimation results are compared between Hill model approaches and newly proposed approach.
Hill-type muscle model
The transformation from EMG to muscle activation is a dominant process because the estimated muscle force is assumed to be proportional to the muscle activation.
EMG processing
The EMG processing method employed in this paper is summarized. For the details, readers can refer to[8, 9].
high-pass filtering of raw EMG using zero-lag 4th order Butterworth filter (30 Hz) to remove motion artifacts
wave rectification
low-pass filtering with a 2 Hz cut-off frequency using zero-lag 2nd order Butterworth filter
normalization with the peak of maximum voluntary contraction (MVC)
There is an alternative method instead of the low-pass filtering of the third step. It is referred to as the mean absolute value (MAV) method. A moving average filter is applied within a fixed time window, which is equivalent to low-pass filtering. The recommendations of the SENIAM project indicate a time window of 0.25 to 0.5 seconds[21]. It corresponds to the choice of cut-off frequency of the low-pass filter of 2 to 4 Hz. This critical parameter choice depends on the task dynamics. Indeed, this choice of cut off frequency is a trade off between how fast the fluctuations in amplitude can be and how reliable the estimate of the amplitude is. A low cut off frequency gives a more reliable estimate of the amplitude, but cannot capture fast changes, while higher cut off frequency results in noisier output but captures fast contractions. The cut off frequency mentioned in the SENIAM recommendations is 2 Hz for slow motions, and 6 Hz for fast motions[21]. In Hill-type model, this cut off frequency should be carefully set depending on the type of task because the envelope of the estimated force is almost determined by this low-pass filtering step. The normalized EMG is processed with the following activation dynamics, which mainly captures the delay from muscle activation to mechanical output.
Activation dynamics
The process of transforming the normalized, rectified and filtered EMG, referred as e(t) to neural activation p(t), is called the activation dynamics as shown in the Figure1 flowchart. When a muscle fiber is activated by a single action potential (AP), the muscle generates a twitch response. This delayed response can be approximated by a critically damped linear second-order differential system[8]. Its recursive discrete form is as follows in Eq. 1 to calculate p(t k ).
p( t k )=γe( t k −d)− β 1 p( t k − 1 )− β 2 p( t k − 2 )
A flowchart of EMG-force estimation by the Hill-type model and multiscale physiological muscle model.
where d is the electromechanical delay and γ, β1 and β2 are the coefficients that define the second-order dynamics. To obtain a positive stable solution, a set of constraints are employed, i.e. β1=C1+C2,β2=C1C2 where |C1|<1,|C2|<1. In addition, the gain of this filter should be maintained by ensuring γ−β1−β2=1 as described in[8].
Nonlinearization of muscle activation for nonlinear Hill model
Many researchers assume that the above p(t) is a reasonable approximation of muscle activation. However, a nonlinear relationship has been reported between individual muscle EMG and the joint moment for some muscles, especially at lower forces[14]. In studies on single motor units, multiple APs cause succesive twitch responses. If the time between APs decreases, twitches start to merge and the muscle force increases steadily. However, at high frequency we reach maximal contraction, where force development saturates even if the frequency increases. This induces a nonlinear relationship between the frequency and force for single motor units[8].
This modification was thus recently introduced to correspond to the mismatch of Hill model estimation at low activation levels. Indeed, in the classical linear Hill model, neural activation p(t) is directly treated as muscle activation a(t) without the nonlinear conversion. When we refer to the modified nonlinear Hill model, the following conversion from neural activation p(t) to muscle activation a(t) is performed as in Figure1. As a simple and adequate solution, Lloyd and Besier[9] proposed the following formulation:
a(t)= e Ap ( t ) − 1 e A − 1
where A is a constant parameter for the nonlinear shape factor allowed to vary between -3 and 0, with A=−3 being highly exponential and A=0 being linear.
Hill-type contraction dynamics
The muscle-tendon unit is modeled as a contractile element in series with an elastic tendon, as shown in Figure2(A) as same as the general Hill model[12]. The tendon element in series was modeled as having a linear stiffness k t of 180 N/mm[22] as the parameter in human triceps surae muscle-tendon complex. ϕ is the pennation angle between the tendon and muscle fibers. Moreover, in this paper, we assess the EMG-force relationship in isometric conditions. Then, only concentric contraction is considered where a contractile element is always shortening during contraction. The parallel elastic element is not included, since its length does not change during isometric contraction, which is assumed in this study. In addition, we would include the parallel element effect in joint level modeling rather than the muscle level when we apply the proposed approach in non-isometric condition. This common macroscopic structure as shown in Figure2(A) is used for the three types of modeling, only the contractile element dynamics change.
Muscle models. (A) Muscle-tendon macroscopic model. (B) Huxley sliding filaments model.
The Hill-type muscle model is used to estimate the force F c (t) generated by the contractile element with the general form:
F c (t)=a(t) f l ( ε c ) f v ( ε ̇ c ) F m
where ε c is the strain of the contractile element, f l (ε c ) and f v ( ε ̇ c ) are the normalized force-length and force-velocity relationships, respectively. Fm is the maximum isometric muscle force.
The force-length relationship shows a Gaussian curve around the optimal length for which the muscle generates the maximum force[12]:
f l ( ε c )=exp − ε c b 2
where b is a constant parameter. f v ( ε ̇ c ) represents the relationship between the velocity and the normalized force. The muscle contracts at its maximum velocity v max without load and slows down as the load increases. In the case of concentric contraction, this relationship can be formulated as follows:
f v ( ε ̇ c )= V sh ( v max + L c 0 ε ̇ c ) V sh v max − L c 0 ε ̇ c
where Lc 0 is the length of contractile element at rest position and V sh is a constant parameter. The b and V sh parameters were obtained from[23, 24]. At each time step, the fiber velocity should be solved and the muscle fiber length is computed by forward integration using Runge-Kutta algorithm[8]. Since the value of ε c changed, the calculation should continue iteratively until the end of the input time series of a(t).
The muscle tendon parameters were adopted from Delp[25]. For gastrocnemius, the parameters are averages for two heads (med/lat). Only EMG of the medial head is measured, but the force of both heads is estimated using the sum of maximum force of two heads through Fm. Fm and pennation angle ϕ are also obtained from[25]. However, in the following, the final result is normalized by the maximum contraction, so the effect of these parameters is not relevant. Parameters of muscle model are detailed in Table1.
Table 1 Muscle model parameters
Physiological muscle model
We breifly describe the main points of the multi-scale physiological muscle model. It integrates the characteristics both of macroscopic and microscopic muscle dynamics. The details of this modeling are given in[19]. This model was originally designed to represent the muscle response under FES and is applied to voluntary muscular force estimation based on EMG in this paper.
Generation of chemical input
The physiological model is composed of two inputs as shown in Figure1. One is the recruitment rate α which determines the percentage of recruited muscle fibers. The recruitment rate α has the same physiological meaning as the neural activation p(t) in Hill-type model. Then normalized p(t) without the nonlinearization was directly used for α. The second input for physiological muscle model is the chemical signal u. It represents the underlying physiological processes between the contraction and relaxation states independent from recruitment. Muscle contraction is initiated by an AP along the muscle fiber membrane, which goes deeply into the cell through T-tubules. It causes calcium release which induces the contraction process when the concentration goes above a threshold and is sustained until the concentration decreases below this threshold again[26]. Hatze[27] gives an example of calcium dynamics [ C a2+] modeling. The contraction-relaxation cycle is then triggered by the [ C a2+] to be defined, and associated with two phases: i) contraction and ii) relaxation. We use a delayed model to take into account the propagation time of the AP and an average delay due to the calcium dynamics. It corresponds to the electromechanical delay for e(t) into p(t) conversion in Hill-type approach. For chemical input generation, the rectified EMG was low-pass filtered with a 30 Hz cut-off frequency. Then, the chemical input u(t) was created by thresholding the extracted EMG signals as shown in Figure3. The signal measured in EMG is the summation of the APs of all different MUs. However, as we are trying to deal with one muscle model for the whole muscle force estimation, the EMG signal was considered as a representative average triggering signal. The thresholding can be assumed to reflect a muscle cell's all-or-nothing response corresponding to the above chemical nature of the muscle contraction process.
Generated chemical input. (A) Filtered rectified EMG signal. (B) Chemical input by thresholding for SOL.
Thus, the chemical threshold consists basically in extracting the contraction time duration of the muscle and to indicating if the muscle is in contraction or not. The level of activation itself is managed by recruitment rate which is classically used in EMG usage for Hill model. Thus, especially for the low activation state, even if the chemical input is inaccurately determined, its effect is low since the recruitment activation p(t) is very low during this period. And for the higher activation state, the contraction period is accurately captured by this method. As long as the threshold is taken little above the baseline, the threshold is less sensitive as long as it gives the picture of contraction/relaxation state. The parameters for chemical input generation was obtained from[18, 28].
Sarcomere scale
All the sarcomeres are assumed to be identical, and the deformation of both sarcomere and muscle is proportional. If S is the sarcomere length, we can write (S−S0)/S0=(L c −Lc 0)/Lc 0=ε c .
Huxley proposed that a cross-bridge between actin filaments and myosin heads could exist in two biochemical states, i.e. attached and detached states as in Figure2(B). Filaments sliding is the result of interactions between the myosin cross-bridges and the thin actin filaments. The cross-bridges reversibly bind to actin and produce a mechanical impulse, which in turn results in force transmission along the filaments. M and A in the figure represents the myosin head and actin binding site respectively.
One myosin head can only attach to one actin site. Then the dynamics of the fraction n(y,t) of the attached cross bridges is given by
∂n ∂t + S 0 h ε ̇ c ∂n ∂y =f(y,t) 1 − n ( y , t ) −g(y,t)n(y,t)
where h is the maximum elongation of the myosin spring, x is the distance and y the normalized distance between the actin binding site and the myosin head:y= x h . n(y,t) is a distribution function representing the fraction of attached cross bridges relative to the normalized relative position y. To ensure a one way displacement, this attachment is considered possible only when y is between 0 and 1. S 0 ε ̇ C represents the velocity of the actin filament relative to the myosin filament. f and g denote the rate functions of attachment and detachment, respectively.
Several f and g functions have been defined and recently a chemical input was introduced by Bestel-Sorine[18] to modify the ability of the cross bridge to attach or not. They also proposed that these rates depend on the relative velocity between actin and myosin. Indeed, the higher the velocity is, the greater the probability to break bridges is. f and g can thus be defined by: During the contraction phase
f ( y , t ) = U c g ( y , t ) = U c + ε ̇ c − f ( y , t )
During the relaxation phase
f ( y , t ) = 0 g ( y , t ) = U r + ε ̇ c
U c and U r are the chemical kinetics levels under contraction and relaxation phases respectively. That can be resumed as below
u ( t ) = Π c ( t ) U c + ( 1 − Π c ( t ) ) U r Π c ( t ) = 1 during contraction, 0 else ( f + g ) ( y , t ) = u ( t ) + ε ̇ c
To complete the description at the sarcomere scale, we assume that the force generated by one attached cross bridge is modeled by a linear spring with constant stiffness. All cross bridges are parallel so the global stiffness and force generated by the whole sarcomere is proportional to the number of formed bridges. Let k0 (Nm −1) denote the maximum stiffness obtained when all the available bridges are attached. Let ξ(y,t) denote the elongation of a cross bridge due to the contribution of the global extension of the sarcomere, where ε c (0) is the initial value, and to the local distribution of elongations y:
ξ(y,t)=y+ S 0 h ε c ( t ) − ε c ( 0 )
The stiffness and force generated by a muscle sarcomere is obtained by computing the first and second moment of the distribution n(ξ(y,t),t)[17]. The two first moments are defined as:
k s ( t ) = k 0 ∫ − ∞ + ∞ n ( ξ ( y , t ) , t ) dy F s ( t ) = k 0 h ∫ − ∞ + ∞ ξ ( y , t ) n ( ξ ( y , t ) , t ) dy
From Eq. 6, it can be rewritten as follows:
k ̇ s = − f + g ( y , t ) k s + k 0 f ( y , t ) F ̇ s = − f + g ( y , t ) F s + k s S 0 ε ̇ c + 1 2 k 0 hf ( y , t )
Contrary to Zahalak approximation, we do not make any assumption on the distribution, rather the chosen f and g functions allows for a straightforward computation of the stiffness and the force generated by the contractile element. It provides a computational effective model that however relies on Huxley theory.
Myofiber and muscle scale
This set of differential equations can be easily extended to the whole muscle fiber considering that each fiber is composed of identical sarcormeres in series. Then k f = k s S 0 L c 0 and F f =F s . In addition, the maximum available cross bridges varied depending on the relative length of the contractile element. This is known as the force-length relationship (f l (ε c )). Contrary to previous studies where this effect is introduced at the macroscopic level, we take into account this relation at the microscopic scale[17]. Indeed, this relation is directly linked to the maximum available actin and myosin sites[16]. For the stiffness k f and the force F f at the fiber scale including Eq. 9, we get:
k ̇ f = − u + ε ̇ c k f + S 0 L c 0 k 0 Π c ( t ) U c f l ( ε c ) F ̇ f = − u + ε ̇ c F f + k f L c 0 ε ̇ c + 1 2 k 0 h Π c ( t ) U c f l ( ε c )
Next, we introduced the recruitment process at muscle scale. At each contraction phase, the recruitment ratio is updated but remains constant during its phase itself. Let k c and F c denote the stiffness and the force for whole contractile element, and N the number of all MUs. The recruited number is written as α N using recruitment ratio α. Finally the complete model of contractile element can be written as a set of differential equations:
k ̇ c = − ( u + ε ̇ c ) k c + α k m Π c ( t ) U c F ̇ c = − ( u + ε ̇ c ) F c + α F m Π c ( t ) U c + k c L c 0 ε ̇ c
where k m =S0N k0f l (ε c )/Lc 0,F m =N k0h f l (ε c )/2. k0 (Nm −1) is the maximum stiffness obtained when all the available bridges are attached.
Recruitment rate is thus mixed with the dynamics of the contraction relaxation cycle providing a wider range of mechanical responses than Hill approaches. Compared to Zahalak approximation, we provide a two input model with intricate dynamics due to both internal muscle dynamics and recruitment rate dynamics in a straightforward way.
Computation of the whole dynamics
For the macroscopic representation, the same configuration with Hill models as in Figure2(A) is used including the muscle tendon parameters. The contractile element is replaced with the above nonlinear differential equations.
The dynamics of the contractile element coupled with the tendon in series should also comply with the following equation:
F ̇ c = k t L t 0 ε ̇ t /cosϕ= k t /cosϕ( L 0 ε ̇ − L c 0 ε ̇ c )
In order to have one solution for two equations of F ̇ c , we must verify that: k t Lc 0/c o s ϕ+k c Lc 0−F c >0. The differential of elongation ε c is computed with the following equation:
ε ̇ c = k t L 0 ε ̇ / cosϕ + F c u − α F m Π c ( t ) U c k t L c 0 / cosϕ + k c L c 0 − S ε ̇ c F c
S ε ̇ c is the sign of ε ̇ c . From the condition: k t Lc 0/c o s ϕ+k c Lc 0−F c >0, S ε ̇ c can be obtained from the sign of these terms: k t L 0 ε ̇ /cosϕ+ F c u−α F m Π c (t) U c . Then we can compute k ̇ c and F ̇ c with (14). The internal state vector of this system should be set asx= k c F c ε c .
Experimental measurement and data processing
Four healthy volunteers (2 males and 2 females, age =30.8±1.3 years, mass =66.5±16.0 kg, and stature =1.7±0.13 m) participated in the study after signing an informed consent form. The study was approved by the Agence française de sécurité sanitaire des produits de santé (Afssaps) committee for persons' protection managed by CHRU Montpellier (2011-A01033-38), dedicated to the study of the lower limb biomechanics within ANR SoHuSim project.
The subjects were seated on a chair with their right foot attached to a Biodex dynamometer (Biodex Medical Systems Inc., New York, USA) as shown in Figure4. The setting was 90° for the ankle joint and 110° for the knee joint, while straps were used on the pelvis and shoulders to secure the subjects' position on the chair. The axis of the dynamometer was aligned with the ankle's rotation axis. The torque around the ankle joint was measured when it was voluntarily generated for the plantar flexion. For EMG measurements, bipolar surface Ag/AgCl-electrodes were placed on the muscle belly of the medial gastrocnemius (GAS) and soleus (SOL) with an inter-electrode distance of 20 m m. The reference electrode was placed on the left patella. Synchronous acquisition of the force and differential EMG signal was performed with a sampling frequency of 2048 H z by the EMG100C amplifier and Biopac MP100 system (Biopac Systems, Inc., Santa Barbara, USA).
Appearance of the experiment. The torque around the ankle joint was measured along with EMG of medial gastrocnemius (GAS) and soleus (SOL) during voluntary plantar flexion.
In this preliminary test, we focused on the isometric condition and tried to compare the output force on a normalized scale against the output during MVC. The common parameters between three approaches were set at the same value as summarized in Table1. In order to maintain similar conditions, the common macroscopic model as in Figure2(A) was used for all the three approaches. Only the dynamics representation of the contractile element was replaced. A was the only parameter identified for each subject. A was obtained by minimizing the root mean square difference between the measured and the model estimation at MVC. Other muscle parameters except A are taken from literatures as explained in the model section.
The estimated muscle force is multiplied by the moment arm in order to obtain the torque. The moment arm was estimated from the Hawkins data[29] from the joint angle in the measured condition. The moment arms for MG and SOL are 0.0515 [m] and 0.0464 [m], respectively. The contribution ratio was calculated using the values reported by Delp[25]. This was obtained as the maximum force by the moment arm considering the pennation angle. The resulting ratio is MG 0.41 vs SOL 0.59. The SUM in the estimation result is plotted using the ratio as the sum of the two muscles.
The generated input command u(t) and activation rate α were given to the contractile element of the physiological model and the active stiffness k c and the muscle force F c were computed using Eq. 14. The same value for F m for the three approaches were used. k m is proportional to F m from the condition in Eq. 14. It was multipied by 20 times, as shown in Table1, based on the knowledge that the muscle active stiffness reaches the tendon stiffness at approximately maximum contraction[30]. However, this muscle stiffness parameter does not influence much the force output in isometric condition.
The predicted joint torque based on EMG signals was compared with the directly measured torque of the ankle joint for the plantar flexion. The comparison was made against the case in MVC in normalized scale. Thus, we avoided the situation effected by the subject-specific difference regarding muscle strength, which is significant for torque estimation in absolute scale.
First, the normalized torques for 30% and 70% of MVC estimated by the nonlinear Hill model and multi-scale model were computed as in Figure5. The normalized data of measured torque (red), normalized estimated torque of SOL (green), normalized estimated torque of GAS (magenta) and the sum of two muscles (blue) are plotted. The obtained RMS errors for the first two subjects are given in Table2 for both contraction levels and by both estimation approaches. The estimation results of physiology based model show the smooth transition compared to the estimation by Hill model keeping the realistic torque development as in the measured torque. In addition, especially for low activation levels, RMS errors in the physiology based model showed better accuracy. Note that the modified Hill model has already the nonlinearization conversion to correspond to a low activation level. The proposed physiological model doesn't include such empirical modifications.
Normalized estimated torques for 30% and 70% of MVC. (red: measured, magenta: estimated by the model of GAS, green: estimated by the model of SOL, blue: sum of both models) (A) result obtained by the modified Hill model. (B) result obtained by the full physiology based model.
Table 2 RMS errors between the measured and estimated results in 30% and 70% of MVC
Next, in order to confirm the estimation ability for both fast-short and slow-long contraction, random contractions including these two types of contraction were requested to all subjects. The generated chemical inputs are shown in Figure3. Normalized torques estimated by linear Hill model (A), nonlinear Hill model (B) and multi-scale physiology based model (C.1) are depicted in Figure6. In the physiological model, active stiffness and strain of the contractile element are explicitly defined in differential equations, then it can be estimated along with the muscle force. The active muscle stiffness k c is shown at Figure6(C.2). The results of linear Hill model is computed to show the contribution of the nonlinear conversion. As we observe the difference between them in Figure6(A,B), nonlinear Hill model estimation is improved especially for low level contractions. However, the errors for fast-short contraction remained high. In contrast, the estimation result as in Figure6(C.1) with the proposed method demonstrates the nice estimation for all conditions of contractions.
Normalized estimated torques for random contraction. (red: measured, magenta: estimated by the model of GAS, green: estimated by the model of SOL, blue: sum of both models) (A) result obtained with the linear Hill model. (B) result obtained with the modified nonlinear Hill model. (C.1) result obtained with the multi-scale physiology based model. (C.2) The corresponding active muscle stiffness k c estimated with the proposed model.
The obtained RMS errors for the four subjects are given in Table3 for both contraction types and by three estimation approaches. In addition, mean error of the peak torque for fast-short contraction are given. Average results is also reported as m e a n±s t a n d a r d deviation over all the subjects. Average error was 16.9% with the linear Hill model, 9.3% with the nonlinear Hill model. In contrast, the error in the multi-scale model was 6.1% while maintaining the uniform estimation performance in both fast and slow contraction speeds. Mean peak error for fast-short contraction are 21.9% with the linear Hill model, 17.6% with the nonlinear Hill model. In contrast, the error in the multi-scale model was 5.8%. In general, results estimated by physiology based model show the best performance both in fast-short and slow-long contractions in these random tests, as reported in Table3.
Table 3 RMS errors between the measured and estimated results in random contraction
Hill-type muscle is a phenomenological model based on experimental facts with no link to the microscopic physiology. First, even if the estimation performances are equivalent, it is meaningful to understand and capture the muscle dynamics with a more detailed representation. Indeed, the same kind of force estimation could be obtained with the newly proposed physiological model using voluntary EMG signals.
Second, previous work by Perreault[13] reported that Hill modeling errors were large for different firing frequencies and greatest at low MU firing rates. The classical linear Hill model was used. They suggested that more physiological coupling between activation and force-velocity properties may help to deal with these issues[13]. Thus, the aim of introducing new approach based on a physiologically detailed model[19] was to develop EMG-force estimation with natural velocity dependency incorporating microscopic cross-bridge dynamics on top of recruitment dynamics.
In general, the estimation accuracy of the proposed method is better than that of the linear and nonlinear Hill-type approach as in the obtained result regarding RMS errors. In addition, uniform accuracy was obtained for different types of contractions in the proposed method. In particular, the proposed model is able to properly deal with both slow and fast contraction speeds. When considering the Hill model results, Figure6(A,B) shows largest errors for the estimation of fast-short contractions. The reason of this error could be explained as follows: the signal measured in EMG is the summation of the APs of all different MUs. Even in the fast-short contraction, the amplitude of EMG itself does not reflect differences except duration with the same level as for slow-long contraction. However, the resulting fast-short contraction force is actually much less than that of slow-long contraction. This means that there is sometimes hysteresis regarding the neural command. In Hill approach, muscle activation a(t) is proportional to the resulting force. It therefore does not include the effect of the time hysteresis in contraction[13], thus Hill model can not accurately represent both short and long-term contractions simultaneously with only one choice of cut-off frequency in EMG low-pass filtering. In the proposed approach, the derivative of the contraction force is directly given by the neural command and it brings time hysteresis in force generation. It is interesting to note that this kind of uniform accuracy for different types of contractions was appeared along with the introduction of cross-bridge dynamics.
In addition, it is known that there is a nonlinear relationship between frequency during contraction and force for single MUs[14]. In modified Hill model, this frequency dependency is offset only by the nonlinear conversion. This nonlinearization was recently proposed to modify the classical Hill model. This process was originally not introduced in the so-called Hill model. In fact, this modification brings much better estimation, especially at lower forces. However, even with the modification, it is not a time function so it still can not correspond to the different muscle contraction speeds. In Hill-type approach, neural activation p(t) is dominantly imposed by low-pass filtering with a 2–10 Hz cut-off frequency. The choice of cut-off frequency is very sensitive to the dynamics of p(t). p(t) is more or less proportional to the resulting force. Therefore, it is advised that the choice of cut off frequency should be carefully selected depending on the type of task in Hill-type modeling. In this study, a 2 Hz cut-off frequency was used according to the SENIAM recommendation. Sometimes by changing the cut-off frequency for different types of contraction, better results can be obtained with Hill-type model. However, in any case, the expected motion by subject is unknown in advance for practical use of EMG-force estimation, so it is difficult to have an appropriate cut-off frequency in advance. Moreover it assumes that the subject stays in the same kind of motion dynamics, that is not always the case. In the proposed model, the nonlinear activation property coming from force-velocity dependency is internally integrated in the model dynamics.
Furthermore, the physiological model satisfies the well established properties observed for the muscle's behavior: i) the force-length relation is included in the definition of Eq. 13, ii) the force-velocity relationship can be expressed in isotonic and tetanic conditions. Before isotonic contraction could occur, the muscle contracts in isometric conditions until the force generated by the muscle balances the imposed one. Isotonic contraction then becomes possible. Assuming F ̇ c =0 in Eq. 14, the following equation may be formulated: F c =(α F m U c + k c L c 0 ε ̇ c )/( U c + ε ̇ c ).
At the beginning of this phase (t = 0), k c (0)≠0 but it depends on the initial isometric contraction phase. If we defineA(0)= L c 0 k c ( 0 ) U c α F m ,B= 1 U c , in concentric contraction where ε ̇ c <0, we get a Hill type force-velocity relation:
F c =α F m 1 + A ( 0 ) ε ̇ c 1 − B ε ̇ c
This correspondence can be found in Eq. 5. It can be verified that our physiological muscle model integrates the force-velocity relation naturally when considering the actin-myosin cross bridge. Finally, this improvement in estimation accuracy is in line with the suggestion put forward in a previous work[13] on introducing crossbridge model.
Future study will be focused on simulations with multiple sets of the proposed model corresponding to multiple MUs in one muscle considering size principal, fast/slow fibers. However, for the application of voluntary EMG-force estimation, we should keep the simplicity as Hill-type model. Future work will also focus on increasing the number of experimental tests and we should develop an algorithm to statistically determine if a muscle is turned on/off regarding the chemical thresholding. In addition, the further interpretation of neuromuscular system both in voluntary and FES activations should be pursued. We have already performed the study on the identification and validation of the physiological model in in-vivo rabbit experiment[31] and in paraplegic subjects including non-isometric situation under FES[32]. In order to be applied to a broader range of clinical situations, further investigations in isokinetic and isotonic cases should be carried out.
In this paper we presented a method that allows estimation of muscle force from EMG signals with a multi-scale physiology based model with a link to underlying microscopic filament dynamics. The experimental results highlight the feasibility of the torque estimation and its comparison with Hill-type models using the same EMG signals. This is the first report on EMG-force estimation based on a multi-scale physiology model integrating Hill-type and microscopic cross bridge representations. The proposed method features:
a novel physiologically detailed model for EMG-force estimation in the place of a phenomenological Hill-type muscle model,
the estimation improvement especially for different types of contraction incorporating dual dynamics of recruitment and microscopic cross-bridge formation toward coupled activation-velocity relationship.
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This work was supported by French National ANR SoHuSim project (http://sohusim.gforge.inria.fr/).
INRIA DEMAR Project and LIRMM, UMR5506 CNRS University of Montpellier, 161 Rue Ada, 34095, Montpellier, France
Mitsuhiro Hayashibe & David Guiraud
Mitsuhiro Hayashibe
David Guiraud
Correspondence to Mitsuhiro Hayashibe.
MH proposed and built the structure of EMG-force estimation with a multi-scale model and wrote the first draft. DG was in charge of the physiological muscle modeling and proof reading. MH performed the scientific computation and experiments. All authors read and approved the final manuscript.
Hayashibe, M., Guiraud, D. Voluntary EMG-to-force estimation with a multi-scale physiological muscle model. BioMed Eng OnLine 12, 86 (2013). https://doi.org/10.1186/1475-925X-12-86
Muscle model
Muscle force estimation
Hill model
Multi-scale physiology


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Father of Myanmar Child Rape Victim Wants Speedy Conclusion to Crime Investigation
NGOs Slam Cambodia's 'Unjustified' Use of Security, Arrests on Anniversary of Commentator's Murder
'Classrooms, Not Cages': Educators Rally Against Detention of Migrant Children
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Home > Entertainment > SRT purge stems from lack of transparency in procurement deals
SRT purge stems from lack of transparency in procurement deals
The abrupt replacement of the State Railway of Thailand governor and board of directors by the prime minister exercising his special powers under Section 44 stemmed from complaints of lack of transparency in project and procurement biddings in the SRT.
Government spokesman Lt-Gen Sansern Kaeokamnerd told a press conference today (Friday) about the purge in the SRT management on Thursday, saying that Prime Minister Prayut Chan-ocha had received a long list of complaints accusing the lack of transparency in the staging of bids for projects and procurement deals in the SRT.
He said the prime minister could not ignore the complaints and had to take the action to purge the SRT management while investigation was underway to determine whether the allegations were substantiated or not.
But for the time being while the probe is underway, the spokesman said that the former governor, Mr Wutthichart Kalayanamit, and the board members were considered as innocent governor until it was proven otherwise.
He stressed that the government and the NCPO had wanted all state agencies to perform their duties in transparency to restore public trust that all the procurement projects were undertaken in a fair and transparent fashion and in compliance with the principle of good governance.
Source: Thai Public Broadcasting Service (Thai PBS)
TGS and Schlumberger Announce New Multi- and Wide-Azimuth Multiclient Reimaging Program in Central U.S. Gulf of Mexico
Poll favours using Section 44 to suppress corruption
Another Canadian Detained in China Amid Diplomatic Chill July 13, 2019
Father of Myanmar Child Rape Victim Wants Speedy Conclusion to Crime Investigation July 12, 2019
NGOs Slam Cambodia's 'Unjustified' Use of Security, Arrests on Anniversary of Commentator's Murder July 12, 2019
'Classrooms, Not Cages': Educators Rally Against Detention of Migrant Children July 12, 2019
Erdogan Faces New Challenger as Party Split Looms July 11, 2019
The Thailand News Day is the famous name in the news industry of the Asean region and has been in the news industry for a long time. The website is to gather and publish news of all kinds on a daily basis and covers almost every industry with aim to provide the ever-changing insights of every industry to its readers.The Thailand News Day is the famous name in the news industry of the Asean region and has been in the news industry for a long time. The website is to gather and publish news of all kinds on a daily basis and covers almost every industry with aim to provide the ever-changing insights of every industry to its readers.

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In its constant quest for new technologies, Apple is rumored to have purchased real-time motion capture company, Faceshift, last week.
The news first surfaced courtesy of MacRumors, who pointed out that it appeared that Faceshift's upper management had been replaced, when three original corporate directors stepped down from their positions. Then the company's social media accounts fell silent, and any mention of their popular Faceshift Studio software has been removed from their website.
A bit of background: Faceshift is a Swiss company that specializes in facial motion capture technology. They have found a way to skirt the traditional facial recognition technology and use a markerless software that analyzes facial expressions and head position in order to create a 3D avatar. From there, users could record facial animation data in real time and export it to other programs and in different file formats. Their most recent endeavor was the production of a Skype plug-in to let users have avatars for live video chat.
Although nothing is confirmed, there are a few reasons that sources believe Apple may have gone ahead and purchased the tech and animation company. For one, the board members who stepped down were replaced by Martin Frey from Baker and McKenzie; a law firm that is frequently used by Apple.
Furthermore, Apple has been pursuing and purchasing small companies that specialize in 3D technology over the past few years. For instance, Apple bought chip-maker PrimeSense in 2013, and a face recognition company called Polar Rose in 2010. In fact, PrimeSense constructed the 3D Carmine sensor that was behind Faceshift's technology, leading a majority of sources to believe that Apple was behind Faceshift's purchase.
For now, Apple and Faceshift are tight-lipped about what a potential partnership could mean. We can speculate that if Apple acquired Faceshift, they may use Faceshift's technology in cooperation with FaceTime, or may bring the technology to cameras in future devices. There's even talk that Apple may want to use this technology in sync with Apple TV for gaming purposes.
But source AppleWorld.Today notes that Apple has had some ideas for the implementation of facial recognition technology for some time now. Previously, Apple acquired patents for "locking and unlocking a mobile device using facial recognition" and a "lens array projector" for 3D mapping.


Foxes are vulnerable to and can transmit a number of serious zoonotic diseases including rabies, mange, canine distemper, parvo enteritis, roundworms, and other endoparasites. They also may be involved in the transmission of arboviral diseases vectored by fleas, ticks, and other external parasites that commonly infest wild animals. If you have seen a fox around your house, you need trapper J's Fox Removal Service.
In many cases, it's not really necessary to trap fox. Proper sanitation to remove attractants and the construction of secure fences, coops, and pens to exclude foxes and protect livestock are usually sufficient to solve most fox problems. Trapper J provides expert fox exclusion services throughout all of Louisiana.
In some cases, fox trapping or fox removal may also be necessary, especially where fox populations are especially high, natural food sources scarce, and fox under increased pressure to find food. Fox trapping and removal is also needed when a fox is suspected of being rabid or otherwise ill. In addition, foxes who have become a little too comfortable around people also should be trapped and removed because they may attack when unintentionally threatened by humans, especially small children, who may try to pet them.
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Tasmanian small businesses are set to benefit from additional opportunities to invest in their business and replace or upgrade their assets, with legislation to extend the $20,000 instant asset write-off passing the Parliament.
"Whether you're a gardener purchasing a ride-on lawn mower, a café owner purchasing an industrial dishwasher or a courier purchasing a vehicle, I encourage all Tasmanian small businesses to take advantage of the $20,000 instant asset write-off – if they're not already," Nationals Senator for Tasmania Steve Martin said.
In the 2015-16 Budget, the Coalition Government increased the immediate deductibility threshold from $1,000 to $20,000.
Backing small business – the engine room of our economy – is part of the Coalition Government's plan for a stronger economy and more jobs.


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The Very Best Private Messaging Apps
by Caroline Corrigan on August 14, 2018
If you'd rather not have the government, hackers, your Internet service provider, or anyone else potentially intercepting and reading your private communications, you should make sure you're using a secure messaging app. Specifically, one that uses encrypted messaging.
As you've probably noticed (unless you live under a rock, which is slowly sounding more and more like the way to go) internet privacy has become one of the hottest topics of the decade. Last year the United States Congress repealed regulations that would help protect your data from being sold by broadband and wireless companies. In 2016, the UK's Parliament passed the Investigatory Power Act (also known as the Snooper's Charter), which expands the surveillance power of the UK Intelligence Community and police. Not to mention what's going on in the news right now concerning privacy. If you aren't already, now is a pretty good time to start wondering just how safe your online communications actually are.
What makes a messaging app secure?
The main thing to check for when choosing a messaging app is whether or not it uses end-to-end encryption. End-to-end encryption means your private chat messages are scrambled, and only the sender and the receiver of the messages have the "keys" to read them. This ensures that no one besides you and the person you're talking to can decipher the messages.
Ironically, encryption used to be thought of as something only used by the paranoid or those with a compelling need for secrecy, such as political dissidents. It was only after whistleblower Edward Snowden leaked classified documents revealing the U.S. NSA's global surveillance program that the world began to fully understand the importance of encryption and online privacy. Since then, many companies (including Facebook, Apple, and Google) have ramped up encryption protection on their software.
Default encryption settings
Just because an app offers end-to-end encryption, doesn't mean that it's the default setting. Some messaging apps require you to go into the app's settings and actually turn on the encryption feature, while others only encrypt messages in certain scenarios (for instance, blue iMessages versus green text messages). Because the importance of encryption is still relatively new, many people may just assume the app is safe without knowing if or when their messages are encrypted.
Open source code
While fears of reverse-engineering or code backdoors may make it seem counter intuitive for an app maker to reveal an app's source code, doing so is now widely regarded as an indicator of the app's integrity. Open source code opens the app up to outside accountability and auditing by experts, which can be a useful way to bring attention to any weaknesses or vulnerabilities in the code.
While many messaging apps today have started using end-to-end encryption, some still collect data information about you, called metadata. Metadata is kind of like your electronic fingerprint, and includes data such as who you talk to (via your contacts list), for how long, and at what time, as well as information about the device you use, your IP address, phone number, and more. Setting up a VPN app on your mobile device, such as AVG Secure VPN for Android or AVG Secure VPN for iOS is an easy way to protect this kind of information.
Popular messaging apps ranked for security
1. Signal
Originally known as TextSecure Private Messenger, Signal has been touted as the gold standard of messaging security by both cryptographer Bruce Schneier and Edward Snowden. Though available as a free messaging app on both iPhone and Android phones, Signal is considered more of a messaging platform because it sends messages across its own data infrastructure.
Signal security features
End-to-end encryption: Like WhatsApp, messages sent via the Signal app can only be viewed by the sender and receiver; not even the company behind the app, Open Whisper Systems, can decrypt the messages. In addition to instant messages, you can also make voice calls, group messages, and encrypted video calls.
Open source: Signal has open source code that can be viewed by anyone. This kind of transparency allows for routine auditing and helps ensure that the app's security is always up to date.
Disappearing messages: For extra security, Signal allows you to make both sent and received messages "disappear" after a certain amount of time has elapsed.
Data storage: Unlike many other messaging apps, Signal only stores the metadata required for the app to work, such as your phone number, random keys, and profile information.
Password security: The app also allows you to set a password to lock it. So even if your phone falls into the wrong hands, your messages will still be protected.
Signal security risks
The best thing about Signal is that there are virtually no security risks. As long as the app's developers continue to be diligent about fixing vulnerabilities, Signal will remain at the top of the messaging app food chain.
2. Wickr Me
Available on both iPhone and Android, Wickr has distinguished itself from the pack by offering secure messaging options for both personal use (Wickr Me) and for businesses and enterprises (Wickr Pro). While Wickr Me is free, Wickr Pro is a paid service that comes with a 30-day free trial.
Wickr Me security features
End-to-end encryption: In addition to encrypted messaging, this year Wickr also announced that its "Me" service will also offer encrypted calling and voice messaging (already offered in the Pro version).
Screenshot detection: Wickr recently announced that they will be offering a new feature that allows users to detect screenshots. This means that you will receive a notification if someone takes a screenshot of a message you send.
Screen overlay protection: On Android devices, Wickr has released a new feature that allows users to disable "Screen Overlays". This prevents users from being able to interact with the app when an overlay is detected, and helps protect the app from TapJacking.
Third party keyboards: On iOS, Wickr lets you block Third Party Keyboards. This helps protect your information by preventing third party keyboards from recording usernames, passwords, and other information that is typed into the app.
Secure Shredder: This feature adds an extra layer of security to making sure your already deleted files can't be recovered with special tools or technology. While Wickr does this for you periodically, you also have the option to manually erase information from your phone.
Wickr Me security risks
Like Signal, Wickr is generally considered almost foolproof from a security standpoint. Though it was previously criticized for keeping its code closed source, last year Wickr finally released its cryptographic protocol on github. If you feel like getting technical about the app's security, you can check out Wickr's Customer Security Promises.
3. Dust
Formerly known as Cyber Dust, Mark Cuban's brainchild messaging app Dust is available on both iOS and Android. The main purpose of the app is to send private messages called "Dusts" to your contacts (though photos and videos can be sent as well); also per the app's name, within 100 seconds of your message being read, it "turns to dust" and disappears. "Blasts" are another type of message that can be sent to a group of people, but are read privately. Finally, you can start group chats, simply known as "Groups".
Dust security features
End-to-end encryption: Dust's encryption model is explained on its website, although the code is not actually available for viewing. You can send encrypted text, photo, or video messages, but the app does not allow for voice or video calls.
No permanent storage: Not only are your messages not permanently saved on your phone or the company's servers (instead they are sent to the app's RAM memory until they are accessed by the receiver), you can also erase your messages off of other people's devices.
Screenshot alerts: If a screenshot is attempted on an Android phone, the name of the person who sent the message is removed, effectively eliminating context from the conversation. Apple prevents apps from blocking screenshots, so instead, iPhone users receive a notification if someone takes a screenshot of their sent message.
Auto "Dust": Messages are automatically erased either within 24 hours, or as soon as they're read. You can choose.
Dust security risks
There are currently no significant security risks associated with Dust, aside from the potential risks and lack of transparency related to the app's code not being open source.
4. WhatsApp
With over one billion users, WhatsApp is one of the most popular messaging apps being used today. The app's popularity is definitely one of its strong points, along with the fact that it's available for free on both iPhone and Android and doesn't show any ads. You can easily send text messages, photos, as well as short video and voice messages. But are WhatsApp chats private?
WhatsApp security features
End-to-end encryption: In April 2016, WhatsApp implemented a super secure encryption protocol developed by Open Whisper Systems (the company behind secure messaging app Signal) across all mobile platforms. Thanks to this protocol, only the sender and receiver have the keys to decrypt messages sent via WhatsApp, meaning they can't be accessed and read by anyone else. Voice and video calls are also encrypted.
Verify encryption: WhatsApp also has a "Verify Security Code" screen in the contact info screen that allows you to confirm that your calls and messages are end-to-end encrypted. The code is presented as both a QR code and a 60-digit number.
Two-step verification: An optional feature, two-step verification allows you to add more security to your account by setting a PIN number that is required to verify your phone number on any device.
Messages not stored: The only time your message is kept on a WhatsApp server is the period after you send it and before it is delivered to the receiver. If it can't be delivered for some reason, then the message is deleted from the server after 30 days.
WhatsApp Security risks
Unencrypted backups: WhatsApp messages can't be intercepted during transmission, but what about message backups on iCloud or Google Drive? The good news for iPhone users is that WhatsApp added encryption protection to iCloud backups in late 2016. But Android phone messages backed up on Google Drive are not encrypted, leaving them potentially vulnerable to hackers, governments that could legally force Google to turn over your messages, or even Google itself. So how can you protect your privacy on WhatsApp as an Android user? Fortunately, you can disable WhatsApp message backups on Google Drive.
Facebook privacy issues: WhatsApp was bought by Facebook in 2014, transferring concerns about the social media conglomerate's reputation for invasive data collection to the messaging app. While Facebook assures users that there is no possible way for them to view encrypted WhatsApp messages, WhatsApp did announce that they would be sharing user metadata with Facebook, for various purposes such as ad-targeting.
5. Telegram
Claiming over 200 million users on both iPhone and Android, Telegram has been steadily growing in popularity since its debut in 2013 and is known for its unique group chat feature that can support up to 100,000 members. Earlier this year, however, a clash with the Russian government over the app makers' refusal to hand over the encryption keys resulted in it being banned in Russia entirely. Telegram has also been viewed as controversial because of its status as the preferred messaging app of ISIS. This has further driven the conversation about what responsibility messaging apps have to work with law-enforcement versus keeping user data fully protected.
Telegram security features
End-to-end encryption: Telegram offers a feature called "Secret Chat" that allows you to protect your messages with end-to-end encryption. However, the feature is not default, so you'll need to know how to turn it on.
Passcode Lock: You can set a 4-digit code to prevent intruders from accessing your messages, which can be useful if your phone gets lost or stolen.
Two-step verification: Found in Settings, two-step verification requires you to use both an SMS code and a password (be sure you know what not to do when creating a password) to log in to the app. You can also set up a recovery email address in case you forget your password).
Open source code: Anyone can check Telegram's source code, protocol, and API to make sure it is up to par.
Telegram Cracking Contest: Telegram challenges "hackers" to attempt to break through their encryption and decipher messages, offering a $300,000 reward for anyone who is able to do so.
Self-destructing messages: Like many other messaging apps, Telegram also offers a Self-Destruct Timer (for Secret Chats only) that will delete private text messages and media within a preset time limit.
Remote logout: Because you can log into Telegram from numerous devices at the same time (web, PC, tablet, smartphone, etc.), the app offers the ability to logout of other sessions from the current device you're using through the Settings menu. This way, if your device is lost or stolen, you can still make sure your messages are secure.
Account self-destruct: After your account has been inactive for a certain amount of time (six months being the default), your account will automatically self-destruct, completely wiping clean all of your messages and media.
Telegram security risks
End-to-end encryption isn't default: You must manually enable Telegram's "Secret Chat" feature, otherwise chats are only encrypted between your device and Telegram's server.
Logging chat data: If you don't enable the Secret Chat feature, then your chat data is saved on Telegram's servers. The company claims this is in case you lose your device and want to recover your messages, but from a security standpoint, this is a big no-no.
Possibly flawed encryption technology: Telegram created its own MTProto protocol, instead of using one that is already proven secure, such as the Signal protocol. Many experts have questioned the reasoning behind this, and have expressed skepticism about the lack of transparency surrounding the protocol.
6. Apple iMessage
The instant messaging service developed by Apple Inc., iMessage is supported by the Messenger application on iOS version 5.0 and later. Allowing users to send text, documents, videos, photos, contact information, and group messages over the Internet, iMessage is very popular among iPhone users (and can only be used between them). We've already gone over tips on how to keep your iPhone safe, but is iMessage actually secure?
iMessage security features
End-to-end encryption: However, iMessage end-to-end encryption only protects messages between iPhone users (which appear in blue). If you send a message to an Android user for instance, the message is sent as a normal text message (in green) and is not encrypted. Unlike many of the other apps on this list, it seems like Apple won't be coming out with iMessage for Android. Though iMessage doesn't directly allow for video or voice calls, its sister app FaceTime does (with encrypted protection).
Self-destructing messages: Many iMessage users are unaware that the app provides a feature that allows you to control how long each photo, video, or message will appear before it's gone. You can also choose how many times the viewer can see the message. However, the feature is only available with iOS 10 and later.
iMessages deleted from servers: Your encrypted messages only remain on Apple's servers for 7 days before they are deleted.
iMessage security risks
Encryption weaknesses: In 2016, researchers at Johns Hopkins University revealed a flaw with Apple's encryption implementation that could leave iMessage vulnerable to the decryption of iMessage messages. While the flaw was quickly patched, it still raised questions about the app's encryption protocol security level, which is closed source and can't be third-party audited.
iCloud backups: If you back up your iMessages to iCloud, these messages are encrypted on iCloud using a key controlled by the company, not you. This means that, if your iCloud is hacked or subpoenaed by a court, they could be revealed. And while Apple has been firm about not creating "back doors" into their system or weakening encryption, they and other tech companies do have a history of cooperating with authorities when it comes to turning over information stored in the Cloud.
7. Facebook Messenger
Facebook's messaging app is available for both iPhone and Android phones, and provides a convenient way to keep up with friends and family thanks to its sheer popularity.
Facebook Messenger security features
End-to-end encryption: In 2016, Facebook added its Secret Conversations feature to secure messages with the Signal end-to-end encryption protocol (also used by WhatsApp). However, Signal and WhatsApp have end-to-end encryption by default, while Secret Conversations must be activated.
Self-destructing messages: You can set Facebook Messenger messages to self-destruct after a certain period of time (between five seconds and 24 hours).
Facebook Messenger security risks
Encryption not by default: As mentioned above, end-to-end encryption for messages must be activated by the user. This means that messages sent without this feature are only encrypted when sent to Facebook's server, and then encrypted again when sent to the recipient (whereas end-to-end is directly between sender and recipient). This means a copy of the message remains on Facebook's servers.
Privacy concerns: In the wake of Facebook's Cambridge Analytica scandal, concerns about Facebook's data collection overreach have only intensified, causing many to wonder how they can protect their personal data while on Facebook. To make matters worse, news broke in March that Facebook has been collecting information about the calls and texts of its Android users through a permission that allows the app to import their phone contacts, confirming doubts that Facebook Messenger is safe and private.
App to avoid: Google Hangouts
Despite being available for free on both iOS and Android, Google Hangouts is riddled with privacy and security concerns. Though it does encrypt hangout conversations, it doesn't use end-to-end encryption — instead, messages are encrypted "in transit". This means that they are only encrypted between your device and Google's servers. Once they are on a server, Google has complete access to them. If ordered to do so, Google can tap into private communication sessions and relay that information to government agencies. And with Google's Transparency Report revealing that the company does indeed receive and often fulfill requests for customer information, this is a very real concern.
Additionally, images sent via Hangouts are shared through public URLs, meaning that virtually anyone (who knows a thing or two about URLs) can view your private images. This is definitely not the app you should be using to send...sensitive...pics.
How can I stay safe?
We believe everyone has a right to online privacy, but sometimes it takes a bit of pro activeness to protect that right from those who would like unfettered access to it. In an ideal world, everyone would be using super secure messaging apps like Signal or Wickr to communicate. But with the popularity of less secure or privacy-questionable apps such as Facebook Messenger or WhatsApp, sometimes the middle ground is more convenient.
If you do choose to use a less secure messaging app, pair it with VPN protection, such as AVG Secure VPN, to keep your information as safe as possible.
Safest Encrypted Messaging Apps for Android & iOS
VPN Privacy Android iOS


An investigative reporting crew released a documentary this month on Al Jazeera showing the protests and negotiations that have occurred regarding mining and hydro-electric projects in the Ngöbe-Bugle territory. The documentary shows the reservoir of the Chan-75 dam which submerged an entire Ngöbe community. Construction is now underway for the Barro Blanco dam, which will submerge more Ngöbe communities. The Barro Blanco region is also the home of a critically endangered endemic species of frog.
Two weeks ago, the government concluded negotiations with the Ngöbe people to pass a new law that prohibits all new mining projects and requires approval of the Ngöbe people for dam projects, but the Barro Blanco dam is excluded from this requirement, despite the protests of the Ngöbe people.
Arua Arua Miria, a Motuan from Hanuabada is an angry man. Over the years, his people have seen their traditional hunting and fishing grounds taken from them through "legal' and illegal means.
Like many other Papua New Guinean communities, Motuan land was communally owned until the arrival of the British in 1884. Commodore James Erskine raised the Union Jack on Motuan land, declared it a protectorate of the British Empire and made laws that – over time – took away the land rights of the traditional landowners.
"The white man arrived just 200 years ago. We were here 2000 years before they arrived.
"They brought their laws – laws that took away our rights. Our ancestors placed their fingerprints on agreements, documents they didn't understand," Miria says.
British Columbia: Oil Spill Response - Canadian Coast Guard "Gone Fishing"
CAUGHT ON VIDEO: Lead federal agency for oil spill response sends fishing party instead of clean-up crew; Gitga'at Nation accuses federal government of mishandling oil spill response in the Great Bear Rainforest.
The leadership of the Gitga'at Nation held an emergency meeting on May 5th to discuss what they see as a failure of the federal government to respond appropriately to an unfolding maritime oil spill in the Great Bear Rainforest. The spill is originating from the Brigadier General M.G. Zalinski, a U.S. army ship that sank in 1946 with 700 tonnes of bunker fuel on board.
The Gitga'at say the government is underestimating the size of the spill and they are demanding a full clean-up of the wreckage.
"News sources today include Indigenous Peoples Issues and Resources and Cultural Survival".


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Altman is president of Social Security Works; Benesch is communications director for the group. They recently wrote the piece "The GOP's tax cut bonanza is a major attack on Medicare," published by Salon.
The piece states: "Do you trust Paul Ryan to protect your Medicare benefits? How about White House budget director Mick Mulvaney, a former member of the House Freedom Caucus, and like Ryan, a longstanding foe of Medicare?
"If the just-passed House tax bill, its Senate counterpart or some compromise of the two is signed into law, the enactment will put Medicare's future in the hands of Ryan and Mulvaney.
"According to the Congressional Budget Office, the GOP tax bill will instantly trigger $400 billion in automatic cuts to Medicare in the next 10 years, including $25 billion in the first year after enactment alone.
"These cuts are the result of a law known as Statutory PAYGO. That law requires an automatic cut in spending when Congress increases the deficit. The tax bill is, in Donald Trump's words, 'a big, beautiful Christmas present' — for Trump's family and other billionaires. If the Republicans are successful in passing a tax bill that increases the deficit by $1.5 trillion, as they intend, the provisions of PAYGO will be activated.
"To be clear: If the tax bill passes the Senate and is signed into law by Trump, nothing more needs to be done to cut Medicare. If the House and Senate do nothing, the cuts take effect immediately after the end of the congressional session and get bigger every passing year. A vote for this tax bill is a vote to cut Medicare.


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Find the flight ticket you need to start planning an unforgettable visit to one of the most vivacious and cultured destinations in Quebec. Enjoy Mount Royal Park, Notre-Dame de Montréal Basilica and the Just For Laughs comedy festival once you've experienced safe, swift air travel from Grande Prairie to Montreal with WestJet.


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County Struggling With Major Solid Waste Losses
Jun 12,2014 by DispatchAdmin
SNOW HILL — The search continues for a policy that will offset some of the money that Worcester County is losing every year to operate its landfills while still avoiding unreasonably high trash deposit fees.
"You're not going to fee your way out of this," said Commissioner Virgil Shockley last week.
Currently, county landfills run at a huge deficit every year and have done so for the past several years. This year the county wants to cut away some of that red ink and has proposed raising the cost of transfer station permits from $60 to $100. It could add a second, pay-as-you-throw option where every trash bag taken to the station would cost $1.
It's worth noting that even with a fee increase, anticipated to generate $175,000 in new revenue, the Solid Waste Enterprise Fund is still expected to operate at a $776,000 loss next year.
However, both options were asking too much of people, according to Pocomoke resident Bill Sparrow.
"People that are on fixed incomes, limited incomes, however you want to take it, are debating today whether they're going to eat anything or buy drugs, the prescriptions that they need," he said. "And you're going to raise this almost 70 percent, $60 to $100. I think it's unfair and it's unreasonable. Do we want our county to look like some of the counties that we're adjacent to with trash all over the road?"
People might resort to dumping everything out on the road rather than paying a higher fee, Commissioner Jim Purnell agreed. He is opposed to a price hike and repeatedly blamed Ocean City withdrawing from the county solid waste program five years ago as the reason why the enterprise fund is in dire straits today. Purnell called on the commission to go back to the drawing board with the fees to find a more palpable alternative.
The rest of the commission wasn't as opposed to raising fees for a variety of reasons with the most common being that solid waste just can't keep operating at a major loss every year. Regarding the possibility of Worcester County becoming one huge field of litter, Commission President Bud Church pointed out that the same warnings had been given years before when the fee was raised to $60 and yet county roads remain, for the most part, trash free.
Church added that it's not fair to put all of the blame for hiked fees on the shoulders of the commission. If people weren't constantly gaming the system and dumping trash from multiple households all under one permit, the transfer stations wouldn't be so far in the red.
It was also time to stop constantly throwing Ocean City under the proverbial bus for the current state of affairs, said Commissioner Louise Gulyas, who represents that district. The resort pulled out of county trash collection program because they were able to find a better deal independently.
"… it's time to get over it and move on," she said.
Gulyas added that even at $100 a year that works out to less than $10 a month to dump trash at a county transfer station, a figure that she felt was affordable even for people on fixed incomes. And if they don't have much trash they could potentially save money by using the pay-as-you-throw option at $1 a bag.
Resident Diana Purnell offered a compromise during last week's meeting, suggesting that the county raise the fees but allow multiple vehicles to use the stations for the $100 annual charge.
"Why not have a household, with two vehicles, be $100?" she asked.
That was a possible alternative, according to Shockley, and one that was worth exploring further. Shockley asked for county staff to figure out how many accounts paid for a second sticker last year. The rest of the commission was also interested in how pay-to-throw programs have been working nationwide. That information will be presented at the next commission meeting in mid-June when a final fee price will be established.
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OCPD Reports Drop In Applicants For 2020 Season
OCEAN CITY – As the Ocean City Police Department prepares for the upcoming summer season, Police Chief Ross Buzzuro shared his recruitment concerns this week.On Monday, Buzzuro presented the Ocean City Police Commission with an update on recruitment efforts for seasonal officers and public safety aides."We have seen a dropoff in candidates," he said, "about… Read more »
Resort Weighs Relaxed Fees, Incentives To Grow Residential Base
OCEAN CITY — Encouraging more year-round residents to make the resort their home by relaxing some of the fees associated with new construction or creating other incentives was the subject of debate this week with a decision made to continue pursuing a program.For several years, the Mayor and Council have heard concerns from property owners… Read more »
Wicomico Opts For Post-Labor Day School Start Date
SALISBURY – The school board voted this week to approve an amended calendar for the 2020-2021 academic year after receiving more than 1,600 responses from the community.On Tuesday, the Wicomico County Board of Education approved on second reading a 2020-2021 school calendar for that will begin the academic year on Sept. 8 and end the… Read more »
Bank's Downtown Branch Donation Will Help Expand OC Museum's Exhibit Space
OCEAN CITY – Efforts to transform a historic downtown building into a museum will move forward thanks to the generosity of a local bank.On Dec. 31, the Bank of Ocean City closed its branch on the corner of Dorchester Street and South Baltimore Avenue. But instead of having the historic building sit empty, officials are… Read more »


What Are Mississippi Marriage Records?
Mississippi marriage records are a commonly searched for type of online and offline document. Many searches only require an online result while others – made to obtain official copies of documents – require that you have a paper copy of the record. If you need official documents, the Mississippi State Department of Health maintains marriage records in their vital records division. You can easily search for the records or request them through a simple form. It takes a bit of time to receive these records through the mail, so if you immediately need to see information contained on a marriage record, then a third party website may be your best chance to review the info quickly.
There are many other reasons why someone might want to view a marriage record in Mississippi. Yours may be the same or different from any of these aforementioned reasons. The important thing you should know is that a marriage records search is extremely simple and easy to carry out thanks to the online tools out there in today's world.
Third party websites maintain gigantic databases of marriage records, for all 50 states. All you have to do is perform a search by name, location, date of marriage, or any other information you might know about the records you're looking for. Type in the information you have and you'll get more than enough results to keep you busy for awhile. Most of the time, you'll luck out and get the exact Mississippi marriage record you need. Other times, you might have to sift through a few results before locating the exact records you want.
People who perform genealogy research love to use marriage records as a way of diving further back into their history. Thanks to the information on there – such as spouse's names and parent's names – it's easy to grab some of the family history just by viewing one marriage record. Date of marriage will be on there as well, so this allows you to view certain periods in your family history that will add to your body of knowledge.
Searching for Mississippi marriage records is an easy thing to do. Hop on the government website for vital records to request an official paper copy of marriage records or simply get on an easy to use third party website to search for the records. Most of the time all you need is first name, last name, and state. From there, you'll be successful.
I am a resident of Ohio and my Sister and her husband who was in the service at that time were married in Gulfport, Miss in 1943. He was in the Army Airforce at that time. They had a child together in 1946 and Harold died from complications in a farming accident in 1947. The child never got to know anything about his Dad so, as his Aunt I am trying to piece together the events in his life in order for his son, who now has a grandson of his own to know a little about his Dad. His Mother, my Sister is now deceased also. The names are Harold G. Hull and Mary Marie Wyscarver, She was an Ohio resident. The wedding took place on Aug 22, 1943, with a Rev. Clark officiating. Any information would be greatly appreciated. Thank You for your consideration.
We are happy to assist you with some insights to locating old Mississippi Marriage Records. Our public records service has over 2 billion public records that go back around 50 years. Since these are older marriage records than are listed in our resources we recommend using a genealogy service, Mississippi state archives or the Mississippi Department of Health. These are all state managed agencies that archive public records going back much further.
Unfortunately those are older MS marriage records than we have in our public records database. There are some other options to explore. Please check out our blog responses, we recently responded to a very similar question.


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Seeking Coherence in Complexity? The Governance of Energy by Trade and Investment Institutions
Arunabha Ghosh
Trade in energy products and services and investments in the energy sector are central to energy security. Despite the rhetoric of energy independence, the world's leading economies inhabit a complex world of energy flows and institutions that seek to govern them. This article asks: how is energy governed by international trade and investment institutions and agreements; and how would it be governed by these institutions depending on alternative governance preferences? Drawing on recent developments, it outlines three sets of tensions – between emerging multipolarity and existing regimes, between states and markets, and a structural imperative between energy and climate – that are shaping the context for energy governance. The article then analyses, from the perspective of energy exporters, importers and firms, how the landscape of multilateral, plurilateral and regional agreements manages these challenges. The current institutional configuration reveals partially overlapping memberships, incoherent rules governing state-driven policies and market-led interventions, and inconsistent rules between energy and environmental concerns. In pursuit of coherence in this complex milieu, the article ends by outlining a schematic framework for institutional design. The design choices depend on countries' preferences for greater or lesser consistency in rules and on more integrated versus fragmented governance across institutions.
Given the pressures of energy security, climate change and trade liberalisation, energy trade and investment cannot be governed through a single institution or regime.
In order to govern the complex structure of energy trade-related institutions, policy makers need to make two sets of decisions: making rules more or less consistent across regimes; and adopting more integrated or more fragmented institutional designs.
A realistic outcome is fragmented governance with more consistent rules: policy makers should aim to liberalise trade in environmental goods and services at the WTO; a climate agreement can offer signals for more low-carbon investments in the energy sector; and Asian institutions (ASEAN+6; APEC) can promote technology cooperation in the region responsible for the most increase in energy demand.
The status quo – fragmented institutions and inconsistent rules – will most likely add to tensions in energy trade: more disputes on state-subsidised clean tech investments; increased competition to secure exclusive access to oil, gas, coal and other minerals; and threats of unilaterally imposed environment-related trade barriers.
Synergies between Energy Efficiency and Energy Access Policies and Strategies
Shonali Pachauri, Diana Ürge-Vorsatz and Michael LaBelle
Internet Fragmentation, Political Structuring, and Organizational Concentration in Transnational Engineering Networks
David Weyrauch and Thomas Winzen
Vol 12, Issue 1, February 2021
Sulfur in the Sky with Diamonds: An Inquiry into the Feasibility of Solar Geoengineering