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

• Published in: Expert systems with applications Vol. 137; pp. 167 - 190
• Main Authors: Wang, Yan, Cang, Shuang, Yu, Hongnian
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2019; Elsevier BV
• Subjects: Deep learning; Features; Healthcare; Human activity recognition; Life expectancy; Machine learning; Mental health; Sensors; Wearable sensors; Wearable technology; Deep learning; Healthcare; Features; Human activity recognition; Wearable sensors
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.04.057

•Discuss limitations and strengths of different sensor modalities.•Focus on the detailed techniques of wearable sensor-based systems.•Survey both hand-crafted and the deep learned features.•Identify the challenges for further improvement. 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.