Real-Time Human Physical Activity Recognition with Low Latency Prediction Feedback Using Raw IMU Data.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date:
Jul 1, 2018
Abstract
In the realm of Human Activity Recognition (HAR), supervised machine learning and deep learning are commonly used. Their training is done using time and frequency features extracted from raw data (inertial and gyroscopic). Nevertheless, raw data are seldom employed. In this paper, a dataset of able-bodied participants is recorded using 3 custom wireless motion sensors providing embedded IMU and sEMG detection and processing and a base station (a Raspberry Pi 3) running a classification algorithm. A Support Vector Machine with Radius Basis Function Kernel (RBF-SVM) is augmented using Spherical Normalization to achieve a motion classification accuracy of 97.35% between 8 body motions. The proposed classifier allows for real-time prediction callback with low latency output.