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Micromachines 2018, 9(9), 450; https://doi.org/10.3390/mi9090450

Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device

1
Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
2
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
3
Graduate Institute of Medical Mechatronics, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
4
Department of Nephrology, Chang Gung Memorial Hospital, Linkou, Tao-Yuan 33305, Taiwan
5
Department of Materials Engineering, Ming Chi University of Technology, New Taipei 24301, Taiwan
*
Author to whom correspondence should be addressed.
Received: 2 August 2018 / Revised: 6 September 2018 / Accepted: 8 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue MEMS Accelerometers)
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Abstract

This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject’s daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services. View Full-Text
Keywords: accelerometer; activity monitoring; regularity of activity; sleep time duration detection accelerometer; activity monitoring; regularity of activity; sleep time duration detection
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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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Lin, W.-Y.; Verma, V.K.; Lee, M.-Y.; Lai, C.-S. Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device. Micromachines 2018, 9, 450.

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