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Sensors 2017, 17(1), 187; doi:10.3390/s17010187

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

Department of Biomedical Engineering, National Yang-Ming University, 155, Li-Nong Street, Section 2, Peitou, Taipei 11221, Taiwan
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Author to whom correspondence should be addressed.
Academic Editors: Ioannis Kompatsiaris, Thanos G. Stavropoulos and Antonis Bikakis
Received: 4 November 2016 / Revised: 3 January 2017 / Accepted: 16 January 2017 / Published: 19 January 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Abstract

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. View Full-Text
Keywords: human motion segmentation; wearable sensors; activities of daily living; automatic activity monitoring human motion segmentation; wearable sensors; activities of daily living; automatic activity monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, K.-C.; Chan, C.-T. Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors. Sensors 2017, 17, 187.

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