Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach

Gesture recognition technology enables machines to understand human gestures. The technology is considered as a key enabler for gaming and virtual reality applications. In this paper, we propose an effective, low-cost capacitive sensor device to recognize hand gestures. In particular, we designed a prototype of a wearable capacitive sensor unit to capture the capacitance values from the electrodes placed on finger phalanges. The sensor captures finger capacitance values. Each gesture has specific finger capacitance values. We applied a running median filter to the output of the sensor and extracted 15 features for gesture classification training and testing tasks. Subsequently, various analyses were performed to provide more insights into the sensing data. We applied and compared two machine learning algorithms: Error Correction Output Code Support Vector Machines (ECOC-SVM) and ${K}$-Nearest Neighbour (KNN) classifiers. The training and testing recognition rates were observed for both intra-participant and inter-participant data sets. Further, we introduced a feature compression approach derived from correlation analysis to reduce the complexity of the machine learning algorithms. Using cross validation, we achieved a classification rate of approximately 99% for intra-participant data. We achieved a lower recognition rate of 97% (average cross validation testing) for compressed feature data set using both machine learning approaches. For the inter-participant data, the recognition rate was 99% (normalized feature data) using KNN and 97% using ECOC-SVM. The research findings show that our recognition system is competitive and has an immense potential for further study.