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A smart device for non-invasive ADL estimation through multi-environmental sensor fusion
This research paper introduces the Smart Plug Hub (SPH), a non-invasive system designed to accurately estimating a patient’s Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and wearable devices that track behavior. However, these appr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567750/ https://www.ncbi.nlm.nih.gov/pubmed/37821665 http://dx.doi.org/10.1038/s41598-023-44436-5 |
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author | Kang, Homin Lee, Cheolhwan Kang, Soon Ju |
author_facet | Kang, Homin Lee, Cheolhwan Kang, Soon Ju |
author_sort | Kang, Homin |
collection | PubMed |
description | This research paper introduces the Smart Plug Hub (SPH), a non-invasive system designed to accurately estimating a patient’s Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and wearable devices that track behavior. However, these approaches have limitations, such as patient memory dependency, privacy violations, and careless device management. To address these limitations, SPH utilizes sensor fusion to analyze time-series environmental signals and accurately estimate a patient’s ADL. We have effectively optimized the utilization of computing resources through the implementation of “device collaboration” in SPH to receive event data and segments portions of the time-series environmental signal. By segmenting the data into smaller segments, we extracted an analyzable dataset, which was processed by an edge device—SPH. We have conducted several experiments with the SPH, and our research has resulted in a significant 75% accuracy in the classification of patients’ kitchen ADLs and an 85% accuracy in the classification of toilet ADLs. These activities include actions such as eating activities in the kitchen and typical activities performed in the toilet. These findings have substantial implications for the progress of healthcare and patient care, highlighting the potential uses of the SPH technology in the monitoring and improvement of daily living activities. |
format | Online Article Text |
id | pubmed-10567750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105677502023-10-13 A smart device for non-invasive ADL estimation through multi-environmental sensor fusion Kang, Homin Lee, Cheolhwan Kang, Soon Ju Sci Rep Article This research paper introduces the Smart Plug Hub (SPH), a non-invasive system designed to accurately estimating a patient’s Activities of Daily Living (ADL). Traditional methods for measuring ADL include interviews, remote video systems, and wearable devices that track behavior. However, these approaches have limitations, such as patient memory dependency, privacy violations, and careless device management. To address these limitations, SPH utilizes sensor fusion to analyze time-series environmental signals and accurately estimate a patient’s ADL. We have effectively optimized the utilization of computing resources through the implementation of “device collaboration” in SPH to receive event data and segments portions of the time-series environmental signal. By segmenting the data into smaller segments, we extracted an analyzable dataset, which was processed by an edge device—SPH. We have conducted several experiments with the SPH, and our research has resulted in a significant 75% accuracy in the classification of patients’ kitchen ADLs and an 85% accuracy in the classification of toilet ADLs. These activities include actions such as eating activities in the kitchen and typical activities performed in the toilet. These findings have substantial implications for the progress of healthcare and patient care, highlighting the potential uses of the SPH technology in the monitoring and improvement of daily living activities. Nature Publishing Group UK 2023-10-11 /pmc/articles/PMC10567750/ /pubmed/37821665 http://dx.doi.org/10.1038/s41598-023-44436-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kang, Homin Lee, Cheolhwan Kang, Soon Ju A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title | A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title_full | A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title_fullStr | A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title_full_unstemmed | A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title_short | A smart device for non-invasive ADL estimation through multi-environmental sensor fusion |
title_sort | smart device for non-invasive adl estimation through multi-environmental sensor fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567750/ https://www.ncbi.nlm.nih.gov/pubmed/37821665 http://dx.doi.org/10.1038/s41598-023-44436-5 |
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