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Home automation and environmental control is a key ingredient of smart homes. While systems for home automation and control exist, there are few systems that interact with individuals suffering from paralysis, paresis, weakness and limited range of motion that are common sequels resulting from severe injuries such as stroke, brain injury, spinal cord injury and many chronic (guillian barre syndrome) and degenerative (amyotrophic lateral sclerosis) conditions. To address this problem, we present the design, implementation, and evaluation of Inviz, a low-cost gesture recognition system for paralysis patients that uses flexible textile-based capacitive sensor arrays for movement detection. The design of Inviz presents two novel research contributions. First, the system uses flexible textile-based capacitive arrays as proximity sensors that are minimally obtrusive and can be built into clothing for gesture and movement detection in patients with limited body motion. The proximity sensing obviates the need for touch-based gesture recognition that can cause skin abrasion in paralysis patients, and the array of capacitive sensors help provide better spatial resolution and noise cancellation. Second, Inviz uses a low-power hierarchical signal processing algorithm that breaks down computation into multiple low and high power tiers. The tiered approach provides maximal vigilance at minimal energy consumption. We have designed and implemented a fully functional prototype of Inviz and we evaluate it in the context of an end-to-end home automation system and show that it achieves high accuracy while maintaining low latency and low energy consumption.

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Virtual reality (VR) systems are increasingly using physiology to improve human training. However, these systems do not account for the complex intra-individual variability in physiology and human performance across multiple timescales and psychophysiological demands. To fill this gap, we propose a theory of multilevel variability where tractable neurobiological mechanisms generate complex variability in performance over time and in response to heterogeneous sources. Based on this theory, we also present a study that examines changes in cardiovascular activity and performance during a stressful shooting task in VR. We examined physiology and performance at three important levels of analysis: task-to-task, block-to-block, session-to-session. Findings indicated joint patterns of physiology and performance that notably varied by the level of analysis. At the task level, higher task difficulty worsened performance but did not change cardiovascular activation. At the block level, there were nonlinear changes in performance and heart rate variability. At the session level, performance improved while blood pressure decreased and heart rate variability increased across days. Of all the physiological metrics, only heart rate variability was correlated with marksmanship performance. Findings are consistent with our multilevel theory and highlight the need for VR and other affective computing systems to assess physiology across multiple timescales.

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Introduction: This paper explores the feasibility of using touchless textile sensors as an input to environmental control for individuals with upper-extremity mobility impairments. These sensors are capacitive textile sensors embedded into clothing and act as proximity sensors. Methods: We present results from five individuals with spinal cord injury as they perform gestures that mimic an alphanumeric gesture set. The gestures are used for controlling appliances in a home setting. Our setup included a custom visualization that provides feedback to the individual on how the system is tracking the movement and the type of gesture being recognized. Our study included a two-stage session at a medical school with five subjects with upper extremity mobility impairment. Results: The experimenting sessions derived binary gesture classification accuracies greater than 90% on average. The sessions also revealed intricate details in participant's motions, from which we draw two key insights on the design of the wearable sensor system. Conclusion: First, we provide evidence that personalization is a critical ingredient to the success of wearable sensing in this population group. The sensor hardware, the gesture set, and the underlying gesture recognition algorithm must be personalized to the individual's need and injury level. Secondly, we show that explicit feedback to the user is useful when the user is being trained on the system. Moreover, being able to see the end goal of controlling appliances using the system is a key motivation to properly learn gestures.

Falls are a major cause of injuries in adults above the age of sixty-five. The economic aftermath of falls and their consequent hospitalization can be huge, totaling more than 30 billion dollars in 2010 alone. A plausible way of mitigating this problem is accurate prediction of future falls and taking proactive remedial action. Spatio-temporal variation in gait is a reliable indicator of a future fall, however, existing systems focus on gait analysis in clinical settings and are not tuned towards continuous gait analysis. In this paper, we present the design of a novel textile capacitive sensor array-based system built into clothing that can reliably capture spatio-temporal gait attributes in a home setting.A key novel research contribution of our work is a context-aware hierarchical signal processing architecture that breaks down the signal processing algorithm into a hierarchy of processing elements. The lower power processing components perform generic feature extraction using observations derived from the capacitor plates, while the higher-level processors aggregate features to infer gait attributes such as stride speed and inter-leg spacing. The system activates the higher power processing elements only when it detects walking. We have prototyped our system using textile capacitive plates built into an ace-bandage and a custom FPGA-based system and show that our system can accurately detect gait attributes that have high correlation with falls, while consuming minimal energy as estimated for a multi-clock-domain 180-nm IC.Research Contributions: The design, implementation, and evaluation of our prototype system presents three novel research contributions.(a) Wearable textile-based capacitive sensor arrays (CSAs) for gait analysis: We present the use of flexible