Fusion of wearable and contactless sensors for intelligent gesture recognition

This paper presents a novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine algorithm. The validation of this method was experimentally carried out using an intelligent learning system that combines two different data sources. The sensors are based on a contactless sensor, which is a radar that detects the movements of the hands and fingers, as well as a wearable sensor, which is a flexible pressure sensor array that measures pressure distribution around the wrist. A hierarchical support vector machine architecture has been developed to effectively fuse different data types in terms of sampling rate, data format and gesture information from the pressure sensors and radar. In this respect, the proposed method was compared with the classification results from each of the two sensors independently. Datasets from 15 different participants were collected and analyzed in this work. The results show that the radar on its own provides a mean classification accuracy of 76.7%, while the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors’ output results with radar using the proposed hierarchical support vector machine algorithm improves the classification accuracy to 92.5%