A Wearable Sensor Network With Embedded Machine Learning for Real-Time Motion Analysis and Complex Posture Detection

Abstract

This work presents an embedded system driven by a wearable sensor network and machine learning to perform complex posture detection and high-precision <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human activity recognition</i> (HAR) in real time. The presented prototype performs real-time HAR using raw data collected from three wireless wearable motion sensor nodes in parallel. The sensors communicate the measured inertial data to a Raspberry Pi 3 running a pre-trained classifier, which performs motion detection and classification in real-time. Our approach based on raw data and machine learning provides more efficiency and simplicity by decreasing the computation cost and the latency. Our detection and classification algorithm utilizes a new custom preconditioning method called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Mapping Spherical Normalization (MMSN)</i> , in combination with a Support Vector Machine with Radial Basis Function Kernel (RBF-SVM). This new preconditioning algorithm allows to sparse the raw inertial data to increase successful classification results without adding any computational burden. The presented approach achieves a motion classification accuracy of 98.28% for 12 body motions, while allowing for real-time prediction with low latency output (< 20 ms which is 50% less than some studies) for preconditioning and processing thanks to the new MMSN preconditioning method and the use of raw data. We tested our approach with 10 able-bodied subjects.