Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey

Abstract

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

arXiv commentAccepted for Publication in IEEE Access DOI: 10.1109/ACCESS.2020.3037715

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• Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition
• Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone under Arbitrary Pedestrian Behavior
• Combining Accelerometer and Gyroscope Data in Smartphone-Based Activity Recognition using Movelets