Smart Suspenders With Sensors and Machine Learning for Human Activity Monitoring

Abstract

Most of the Human Activity Recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This paper, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement’s periodicity, resulting in less noisy readings with non-localized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the pre-processing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with best accuracy value of 97.85%. A comparison of the performance between kernel and linear discriminant analysis (KDA and LDA) for this system is made and KDA outperformed LDA across most classifiers.