An Efficient Human Activity Recognition Framework Based on Wearable IMU Wrist Sensors

Abstract

Lately, Human Activity Recognition (HAR) using wearable sensors has received extensive research attention for its great use in the human health performance evaluation across several domain. HAR methods can be embedded in a smart home healthcare model to assist patients and enhance their rehabilitation process. Several types of sensors are currently used for HAR amongst them are wearable wrist sensors, which have a great ability to deliver Valuable information about the patient's grade of ability. Some recent studies have proposed HAR using Machine Learning (ML) techniques. These studies have included non-invasive wearable wrist sensors, such as Accelerometer, Magnetometer and Gyroscope. In this paper, a novel framework for HAR using ML based on sensor-fusion is proposed. Moreover, a feature selection approach to select useful features based on Random Forest (RF), Bagged Decision Tree (DT) and Support Vector Machine (SVM) classifiers is employed. The proposed framework is investigated on two publicly available datasets. Numerical results show that our framework based on sensor-fusion outperforms other methods proposed in the literature.