Abstract
This brief presents a wireless smart glove based on multi-channel capacitive pressure sensors that is able to recognize 10 American Sign Language gestures at the edge. In this system, 16 capacitive sensors are fabricated on a glove to capture the hand gestures. The sensor data is captured by a 16-channel CDMA-like capacitance-to-digital converter for training/inference at the edge device. Unlike the conventional approach where the capacitive information is recovered before further signal processing, our proposed system approach takes advantage of the capability of the machine learning (ML) algorithms and directly processes the code-modulated signals without demodulation. As a result, it reduces the input data throughput fed into the ML algorithms by $20\times $ . The on-site ML implementation significantly reduces decision-making latency and lowers the required data throughput for wireless transmission by at least $4\times $ . The highest testing classification accuracy of our system achieved is 99.7%, with a <0.1% difference from the conventional demodulated sensing scheme.
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