Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition

Abstract

A novel approach of fusing datasets from multiple sensors using a hierarchical support vector machine (HSVM) algorithm is presented. The validation of this method is 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 HSVM architecture is 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 is compared with the classification results from each of the two sensors independently. Herein, datasets from 15 different participants are collected and analyzed. The results show that the radar on its own provides a mean classification accuracy of 76.7%, whereas the pressure sensors provide an accuracy of 69.0%. However, enhancing the pressure sensors' output results with radar using the proposed HSVM algorithm improves the classification accuracy to 92.5%.