Effects of End-effector Type Robot Assisted Gait Therapy on Gait Pattern and Energy Consumption in Chronic Post-stroke Hemiplegic Patients
Effects of End-effector Type Robot Assisted Gait Therapy on Gait Pattern and Energy Consumption in Chronic Post-stroke Hemiplegic Patients
Restoration of gait independence in stroke patients is one of the most important goals of
 rehabilitation therapy, and gait rehabilitation is one of the most important treatments in
 the treatment of stroke because it is a major factor affecting rehabilitation after stroke.
 In the rehabilitation of patients with post - stroke walking disorders, previous physical
 therapy was mainly manual therapy using therapist 's physical effort and walking training
 with walking aids. In recent years, however, emphasis has been placed on therapies based on
 motor learning concepts, which allow the patient to intensively train the exercise as closely
 as possible to the ultimate goal.

 The robot used for walking rehabilitation includes exoskeleton walking robot such as Lokomat®
 (Hocoma AG, Switzerland), Walkbot-G® (P & S Mechanics, Korea), MorningWalk® (Curexo, Korea)
 According to the Systematic Review, which compares two types of robot-assisted gait treatment
 divided into end-effector type, which is not an exoskeletal type such as System® (Rehatech,
 Switzerland) It has been reported that the percentage of patients who were able to walk
 independently when treated with a robot was higher than that of an exoskeleton-type robot.

 In this regard, in terms of acquisition of independent gait, studies on the therapeutic
 effect of the exoskeleton-type robot and the end-effector-type robot before and after the
 gait therapy were continuously performed, but 80% of the patients obtained independent gait,
 Despite the fact that many of these patients have abnormal walking, research has not yet been
 conducted. In previous studies, there was a statistically significant improvement in
 parameters of Gait speed, Cadence, and step length when compared with spatiotemporal
 parameters in training using exoskeleton robots for stroke patients. In another study, Gait
 speed and Cadence did not show a statistically significant improvement, and the effect on
 Gait speed and Cadence is still unknown. However, unlike exoskeletal robots, end-effector
 robotic gait training has been reported to improve Gait speed in most studies compared to
 conventional gait training. In addition, Cadence, Temporal symmetry ratio, Single, an
 improved side stride length, an improvement in the symmetry index of stance phase, and an
 improvement in Gait endurance.

 In this way, the end effector type robot walking training is more likely to improve walking
 quality than the exoskeleton type robot. The end-effector type robot, which is different from
 the exoskeleton type, reproduces the gait using the ankle joint to induce the movement of the
 knee joint and the hip joint. Therefore, it is possible to control the ankle joint, which is
 essential for improving the gait pattern. It is considered that the end effector type robot
 which can control the ankle joint is more likely to induce the improvement of the gait
 pattern than the existing exoskeleton type robot because it shows limitations in reproducing
 the ankle rocker motion.
There are few studies on kinematic, kinematic, and energy consumption after robot training,
 so it is urgent to study this part. In a small retrospective open-label study, the results of
 spatiotemporal parameters and kinetic and kinematic analyzes of patients with chronic stroke
 in patients who underwent gait using an end-effector robot were compared with those of Gait
 speed, Cadence , Stride time, and stride speed, improvement of hip extension in kinematic
 analysis as a whole, and reduction of anterior tilting in pelvis. This suggests that
 robot-assisted gait training may improve the kinematic index Randomized Controlled Trial
 design is a systematic study.

 In addition, it is important to evaluate the energy expenditure and cardiorespiratory load of
 robot-assisted walking therapy for the rehabilitation of patients at risk of cardiovascular
 disease and stroke patients with impaired cardiopulmonary function. The purpose of gait
 therapy in stroke patients is to improve the efficiency of energy consumption by calibrating
 patterns of gait and asymmetry of gait movements. This is also an important issue for gait
 researchers.

 The authors reported that when using an end-effector type robot, the oxygen consumption was
 statistically significantly lower during the robot-assisted walking compared to when the
 robot was not assisted by the robot. During the walking with the exoskeleton type robot, and
 when compared to OTW (Overground treadmill walking) during ATW, there was a statistically
 significant decrease in mean oxygen consumption There was a report. However, previous
 researches did not compare the pre - treatment and post - treatment, but there is no report
 on the possibility of improvement of oxygen consumption after robot - assisted gait training.

 In this study, we divided the patients into two groups. One group was treated with 6-week
 gait training using an end-effector type robot-assisted walking device and the other group
 was treated with gait therapy for the same period of time. Six weeks after the end of the
 treatment, three-dimensional motion analysis, foot pressure analysis and energy consumption
 analysis were performed to obtain robot assisted training in terms of space time index,
 kinematics, kinematic index, dynamic EMG activation pattern, The purpose of this study was to
 investigate whether the improvement in walking performance and the energy consumption
 efficiency of walkers are more effective than the conventional walking training group.

 the three most natural walking cycles Calculate kinematical index and spatio-temporal index
 according to each gait cycle

 Dynamic EMG analysis Dynamic EMG was performed by attaching surface EMG to the skin using
 Medial GCM, Tibialis Anterior, Vastus Medialis, Rectus Femoris, Medial Hamstring, and Gluteus
 Maximus of both lower limbs using a wireless Delsys Trigno Sensor System (Delsys Inc, USA)
 Measure the signal and convert it to Root mean square (RMS). (Figure 5) EMG signal sampling
 rate: 2000 samples / sec Filter: EMG signal bandwidth 20- 450 Hz Surface electrode: Parallel
 bar electrode

 The measured EMG signals are obtained by measuring the duration and the period of activity
 according to the walking cycle and analyzing the degree of activation.

 1. Medial GCM, Tibialis Anterior, Vastus Medialis, Rectus Femoris, Medial Hamstring, and
 Gluteus Maximus

 2. Starting and ending points of muscle activation cycle

 3. Muscle activation duration and RMS integral and peak value

 4. The root mean square (RMS) value divided by 16 sections divided by time

 5. Comparison between the right side and the left side

 2-2. Energy consumption analysis Use K4b2 (COSMED, Italy) as a wearable metabolic system
 (Fig. 6) Measure O2 cost [ml / (m / kg)] and O2 rate (ml / min / kg) The walking distance was
 measured by walking with the self-selected gait velocity while wearing K4b2 (COSMED, Italy)
 for 5 minutes in total. The walking distance was measured for 3 minutes except the first 1
 minute and the last 1 minute of oxygen consumption data for 5 minutes Using O2 rate and O2
 cost

 2-3. Foot pressure analysis The foot pressure was measured using a F-Scan system (Tekscan,
 USA) with a 0.16-mm thick, 980 force-sensing resistors (3.88 sensors per centimeter square)
 After inserting the pressure insoles, calibrate them according to the Tekscan user manual
 (Tekscan Research Software User Manual version 6.7 Rev. D, 2003) and measure them and analyze
 them as follows.

 2-4. Fugl-Meyer Assessment(FMA) for Lower extremities 2-5. 10m walking test 2-6. Berg balance
 scale(BBS) 2-7. Timed up and go test(TUG) 2-8. Functional Ambulation Category(FAC) 2-9.
 Modified Ashworth Scale(MAS) 2-10. Rivermead Mobility Index(RMI) 2-11. Functional
 independence measure(FIM)
Inclusion Criteria:

 -  Stroke patient visited Shinchon Severance Hospital Rehabilitation Department

 -  Adults over 19 years

 -  Ischemic or hemorrhagic stroke confirmed by brain magnetic resonance imaging or
 computed tomography

 -  Patients who have had a stroke for more than 3 months

 -  Those who have hemiplegia after a stroke

 -  If the walking pattern is abnormal and the walking speed is less than 0.8m / sec

 -  Those who have a score of K-MMSE score of 24 or higher in the Korean version

 -  A person who can walk independently with 3 or more points in the Functional Ambulation
 Category (FAC) classified as 0 ~ 5 according to the degree of need for assistance in
 walking

 -  The patients who understand the research and have voluntary participation

 Exclusion Criteria:

 -  Those who have difficulty walking before stroke

 -  Modified Ashworth scale of the lower extremity muscle is 3 or more

 -  Patients with ataxia

 -  Severe lower extremity joints, osteoporosis, and untreated fractures.

 -  Patients who weigh more than 135kg

 -  Damage of the skin in contact with the machine during robot walking

 -  Patients who underwent orthopedic or neurosurgical surgery within 6 months of the
 start of the study

 -  uncontrolled hypertension or orthostatic hypotension

 -  Patients who are likely to spread pathogenic microorganisms due to contact

 -  Not cutting

 -  Cardiovascular disease, venous thrombosis or heart failure, respiratory disease

 -  Malignant neoplasm

 -  Other basic diseases that can not tolerate robot assisted walking

 -  If the tester is judged as not suitable for this study