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2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2015)
St. Louis, MO, USA
March 23, 2015 to March 27, 2015
ISBN: 978-1-4799-8033-8
pp: 198-206
Gurashish Singh , Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Alexander Nelson , Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Ryan Robucci , Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Chintan Patel , Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Nilanjan Banerjee , Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
ABSTRACT
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.
INDEX TERMS
Capacitive sensors, Sensor arrays, Gesture recognition, Capacitance, Capacitors, Signal processing algorithms
CITATION

G. Singh, A. Nelson, R. Robucci, C. Patel and N. Banerjee, "Inviz: Low-power personalized gesture recognition using wearable textile capacitive sensor arrays," 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)(PERCOM), St. Louis, MO, USA, 2015, pp. 198-206.
doi:10.1109/PERCOM.2015.7146529
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