Adaptive and Personalized Gesture Recognition Using Textile Capacitive Sensor Arrays

Upper extremity mobility impairment is a common sequel of Spinal Cord Injury (SCI), brain injury, strokes, and degenerative diseases such as Guillain-Barré and ALS. Existing assistive technology solutions that provide access as user input devices are intrusive and expensive, and require physical contact that can have deleterious effects such as skin friction injury for paralyzed users who have reduced skin sensitivity. To address this problem, in this paper, we present the design, implementation, and evaluation of a non-contact proximity gesture recognition system using fabric capacitive sensor arrays. The fabric sensors are lightweight, flexible, and can be easily integrated into items of quotidian use such as clothing, bed sheets, and pillow covers. Our gesture recognition algorithm builds on two known classification techniques, Hidden Markov Model and Dynamic Time Warping to convert raw capacitance values to alphanumeric gestures. Our system is personalized to the user, allowing personalized selection of gesture sets and definition of gesture patterns in accordance with their capabilities. Our system adapts to changes in sensor configuration and orientation with minimal user training and intervention. We have evaluated our system in the context of a gesture-driven home automation system on six subjects that includes an individual who has a C6 Spinal Cord injury. We show that our system can recognize gestures of varying complexity with an average accuracy of 99 percent with minimal training.