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A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users
This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705856/ https://www.ncbi.nlm.nih.gov/pubmed/34960371 http://dx.doi.org/10.3390/s21248275 |
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author | Fu, Jicheng Zhang, Shuai Wang, Hongwu Zhao, Yan Daniel Qian, Gang |
author_facet | Fu, Jicheng Zhang, Shuai Wang, Hongwu Zhao, Yan Daniel Qian, Gang |
author_sort | Fu, Jicheng |
collection | PubMed |
description | This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices. |
format | Online Article Text |
id | pubmed-8705856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87058562021-12-25 A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users Fu, Jicheng Zhang, Shuai Wang, Hongwu Zhao, Yan Daniel Qian, Gang Sensors (Basel) Article This study is motivated by the fact that there are currently no widely used applications available to quantitatively measure a power wheelchair user’s mobility, which is an important indicator of quality of life. To address this issue, we propose an approach that allows power wheelchair users to use their own mobile devices, e.g., a smartphone or smartwatch, to non-intrusively collect mobility data in their daily life. However, the convenience of data collection brings substantial challenges in data analysis because the data patterns associated with wheelchair maneuvers are not as strong as other activities, e.g., walking, running, etc. In addition, the built-in sensors in different mobile devices create significant heterogeneity in terms of sensitivity, noise patterns, sampling settings, etc. To address the aforementioned challenges, we developed a novel approach composed of algorithms that work collaboratively to reduce noise, identify patterns intrinsic to wheelchair maneuvers, and finalize mobility analysis by removing spikes and dips caused by abrupt maneuver changes. We conducted a series of experiments to evaluate the proposed approach. Experimental results showed that our approach could accurately determine wheelchair maneuvers regardless of the models and placements of the mobile devices. MDPI 2021-12-10 /pmc/articles/PMC8705856/ /pubmed/34960371 http://dx.doi.org/10.3390/s21248275 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fu, Jicheng Zhang, Shuai Wang, Hongwu Zhao, Yan Daniel Qian, Gang A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title | A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title_full | A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title_fullStr | A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title_full_unstemmed | A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title_short | A Novel Mobile Device-Based Approach to Quantitative Mobility Measurements for Power Wheelchair Users |
title_sort | novel mobile device-based approach to quantitative mobility measurements for power wheelchair users |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705856/ https://www.ncbi.nlm.nih.gov/pubmed/34960371 http://dx.doi.org/10.3390/s21248275 |
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