Smart System for Recognizing Daily Human Activities Based on Wrist IMU Sensors

Abstract

Recognizing daily human activity using machine learning techniques is of great interest to many researchers working in the field of human health monitoring. Recently, wearable sensors have been used extensively for human activity recognition (HAR) for their great ability for capturing human actions during his daily life. Wearable wrist sensors have the advantage of being easily and comfortably worn. Extracting multimodal data from such sensors could enhance recognition rates leading to a healthier life style. Machine learning (ML) techniques have exciting capabilities, and can be used to facilitate HAR process. In this paper, a new daily HAR system is proposed for accurately recognizing daily human activity based on multimodal data from a wearable IMU wrist sensor. Two publically available datasets are employed to examine its effectiveness. The results indicate that the proposed HAR system is competitive to other recent related HAR approaches. This proves that the proposed HAR system is robust and, can be used for health monitoring applications.