A survey on wearable sensor modality centred human activity recognition in health care

Wang, Yan; Cang, Shuang; Yu, Hongnian

Abstract

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR.

Citation

Wang, Y., Cang, S., & Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137, 167-190. https://doi.org/10.1016/j.eswa.2019.04.057

• Journal Article Type: Review
• Acceptance Date: Apr 24, 2019
• Online Publication Date: Apr 25, 2019
• Publication Date: 2019-12
• Deposit Date: Jan 9, 2020
• Publicly Available Date: Apr 26, 2020
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 137
• Pages: 167-190
• DOI: https://doi.org/10.1016/j.eswa.2019.04.057
• Keywords: Human activity recognition; Wearable sensors; Deep learning; Features; Healthcare
• Public URL: http://researchrepository.napier.ac.uk/Output/2354631