You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared with the existing techniques.

Cited Abstract(s):
[1]: The ability to assess fine-scale user responses has applications in advertising, content creation, recommendation, and psychology research. Unfortunately, current approaches, such as focus groups and audience surveys, are limited in size and scope. In this paper, we propose a combined biometric sensing and analysis methodology to leverage audience-scale electro-dermal activity (EDA) data for the purpose of evaluating user responses to video. We provide detailed characterization of how temporal physiological responses to video stimulus can be modeled, along with first-of-its-kind audience-scale EDA group experiments in uncontrolled real-world environments. Our study provides insights into the techniques used to analyze EDA, the effectiveness of the different temporal features, and group dynamics of audiences. Our experiments demonstrate the ability to classify movie ratings with accuracy of over 70 on specific films. Results of this study suggest the ability to assess emotional reactions of groups using minimally invasive sensing modalities in uncontrolled environments.

[2]: Central feedback of peripheral states of arousal influences motivational behavior and decision making. The sympathetic skin conductance response (SCR) is one index of autonomic arousal. The precise functional neuroanatomy underlying generation and representation of SCR during motivational behavior is undetermined, although it is impaired by discrete brain lesions to ventromedial prefrontal cortex, anterior cingulate, and pariet al lobe. We used functional magnetic resonance imaging to study brain activity associated with spontaneous fluctuations in amplitude of SCR, and activity corresponding to generation and afferent representation of discrete SCR events. Regions that covaried with increased SCR included right orbitofrontal cortex, right anterior insula, left lingual gyrus, right fusiform gyrus, and left cerebellum. At a less stringent level of significance, predicted areas in bilateral medial prefrontal cortex and right inferior pariet al lobule covaried with SCR. Generation of discrete SCR events was associated with significant activity in left medial prefrontal cortex, bilateral extrastriate visual cortices, and cerebellum. Activity in right medial prefrontal cortex related to afferent representation of SCR events. Activity in bilateral medial prefrontal lobe, right orbitofrontal cortex, and bilateral extrastriate visual cortices was common to both generation and afferent representation of discrete SCR events identified in a conjunction analysis. Our results suggest that areas implicated in emotion and attention are differentially involved in generation and representation of peripheral SCR responses. We propose that this functional arrangement enables integration of adaptive bodily responses with ongoing emotional and attentional states of the organism.

[3]: We conducted a 19 participant study using a system comprised of wireless galvanic skin response (GSR), heart rate (HR), activity sensors and a mobile phone for aggregating sensor data and enabling affect logging by the user. Each participant wore the sensors daily for five days, generating approximately 900 hours of continuous data. We found that analysis of emotional events was highly dependent on correct windowing and report results on synthesized windows around annotated events. Where raters agreed on the timing and quality of the emotion we were able to recognize 85 of the high and low energy emotions and 70 of the positive and negative emotions. We also gained many insights regarding participant's perception of their emotional state and the complexity of emotion in real life.

[4]: The analysis of correlated point process data has wide applications, ranging from biomedical research to network analysis. In this work, we model such data as generated by a latent collection of continuous-time binary semi-Markov processes, corresponding to external events appearing and disappearing. A continuous-time modeling framework is more appropriate for multichannel point process data than a binning approach requiring time discretization, and we show connections between our model and recent ideas from the discrete-time literature. We describe an efficient MCMC algorithm for posterior inference, and apply our ideas to both synthetic data and a real-world biometrics application.

[5]: Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81 and 76 accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones.

[6]: Abstract Sweat secretion from individual sweat glands on the human sole was observed in four male subjects by using a videomicroscope and correlated with sudomotor neural activity recorded from the tibial nerve by means of microneurography. Individual sweat glands could be distinguished as active, less active and inactive according to the incidence of sweat secretion during spontaneous sweating. The threshold amplitude of the sudomotor burst necessary for sweat secretion varied from gland to gland. The number of sweat secretion was significantly related to the threshold amplitude. Sweat glands often failed to produce sweat secretion even when a suprathreshold burst occurred: only 46.1±3.8 (mean±S.E.M.) of the suprathreshold bursts elicited sweat secretion. Failure of the sweat secretion tended to appear after several bursts occurred consecutively with short intervals. In spite of the variability in sweat gland activity, the number of sweat glands recruited was linearly related to the amplitude of the sudomotor burst ( P

[7]:
The study of Electrodermal Activity signals, or EDA signals, dates back to the early @math century ( e.g., [7] ) with the observation of a connection between changes in user skin conductance and psychological state. In recent years, this connection has been validated by examining brain function via fMRI and skin conduction via EDA concurrently in [2] , and by showing the specific regions of the brain that correspond with EDA changes and video recordings of sweat glands in [6] . The promise of EDA as a window into user psychology resulted in extensive work on evaluating the connection between EDA and user interactions [3] , stress detection [5] , content and audience segmentation [4] , and reaction to video content [1] ---to name only a few.