Gesture Classification with Low-Cost Capacitive Sensor Array for Upper Extremity Rehabilitation

Machine Learning and artificial intelligence play major roles in understanding human activity through various classification and regression tasks. However, for many lowresource devices, high computation cost resulting from the construction of AI models may limit their applications. To that end, this work explores gesture recognition through a low-cost capacitive sensor matrix overlayed on a rehabilitation activity table. For gesture recognition, a convolutional long short-term memory (C-LSTM) neural network structure is applied and hyper-parameters are varied to determine what resources are necessary to perform classification tasks. The 8 X 8 mutual capacitive sensor array (CSA) is constructed with low-cost copper adhesive. The designed capacitive sensors capture hand motions performed by patients during rehabilitative exercise. The motions cause changes in the electric field that is quantified through sampling the changing capacitance between the copper tape electrodes. An MSP430 MCU computes the capacitance-todigital conversion at a 50 Hz sampling rate. To identify low computation cost models for the C-LSTM neural network, we evaluate different numbers of capacitor sensors, kernels, convolutional layers, and hidden nodes. Six subjects performed 1200 gestures, and the accuracy metrics are calculated using fivefold cross-validation.