Event-Driven Low-Power Gesture Recognition Using Differential Capacitance

摘要

Individuals with mobility impairments represent a large portion of the population. These impairments are often the sequels of strokes, spinal cord injury, or diseases, such as amyotrophic lateral sclerosis and Guillain–Barré syndrome. Many of these diagnoses manifest in upper-extremity mobility impairment, which may prohibit or make difficult actuating devices within the home. While solutions exist for smart-home automation and control, a few are approachable by those with mobility impairments. To address this issue, we designed Inviz, a touchless low-power assistive gesture recognition system that utilizes an array of textile capacitive sensors to control a smart-home automation system. We present two novel research contributions; the use of flexible textile-based capacitive arrays as differential proximity sensors, and a low-power event-driven hierarchical signal processing algorithm for capacitor sensor arrays. A fully functional prototype system is implemented and evaluated in the context of an end-to-end home automation system using intuitive swipe and hover gestures that are comfortable and approachable for users with mobility impairments. The evaluation demonstrates that the system achieves high accuracy (>90%) while maintaining low latency (<1 s) and low energy consumption (2475 ). In addition, we evaluate the system on a subject with upper extremity mobility impairment to verify its usage as an accessibility device.