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Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187390/ https://www.ncbi.nlm.nih.gov/pubmed/30424383 http://dx.doi.org/10.3390/mi9090450 |
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author | Lin, Wen-Yen Verma, Vijay Kumar Lee, Ming-Yih Lai, Chao-Sung |
author_facet | Lin, Wen-Yen Verma, Vijay Kumar Lee, Ming-Yih Lai, Chao-Sung |
author_sort | Lin, Wen-Yen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6187390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61873902018-11-01 Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device Lin, Wen-Yen Verma, Vijay Kumar Lee, Ming-Yih Lai, Chao-Sung Micromachines (Basel) Article 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. MDPI 2018-09-10 /pmc/articles/PMC6187390/ /pubmed/30424383 http://dx.doi.org/10.3390/mi9090450 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lin, Wen-Yen Verma, Vijay Kumar Lee, Ming-Yih Lai, Chao-Sung Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title | Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title_full | Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title_fullStr | Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title_full_unstemmed | Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title_short | Activity Monitoring with a Wrist-Worn, Accelerometer-Based Device |
title_sort | activity monitoring with a wrist-worn, accelerometer-based device |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187390/ https://www.ncbi.nlm.nih.gov/pubmed/30424383 http://dx.doi.org/10.3390/mi9090450 |
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