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• W2345200750 abstract "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 μW). In addition, we evaluate the system on a subject with upper extremity mobility impairment to verify its usage as an accessibility device." @default.
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• W2345200750 date "2016-06-01" @default.
• W2345200750 modified "2023-10-05" @default.
• W2345200750 title "Event-Driven Low-Power Gesture Recognition Using Differential Capacitance" @default.
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• W2345200750 doi "https://doi.org/10.1109/jsen.2016.2530805" @default.
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