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Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor

Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart...

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Autores principales: Khwaounjoo, Prashanna, Singh, Gurleen, Grenfell, Sophie, Özsoy, Burak, MacAskill, Michael R., Anderson, Tim J., Çakmak, Yusuf O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230824/
https://www.ncbi.nlm.nih.gov/pubmed/35746395
http://dx.doi.org/10.3390/s22124613
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author Khwaounjoo, Prashanna
Singh, Gurleen
Grenfell, Sophie
Özsoy, Burak
MacAskill, Michael R.
Anderson, Tim J.
Çakmak, Yusuf O.
author_facet Khwaounjoo, Prashanna
Singh, Gurleen
Grenfell, Sophie
Özsoy, Burak
MacAskill, Michael R.
Anderson, Tim J.
Çakmak, Yusuf O.
author_sort Khwaounjoo, Prashanna
collection PubMed
description Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.
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spelling pubmed-92308242022-06-25 Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor Khwaounjoo, Prashanna Singh, Gurleen Grenfell, Sophie Özsoy, Burak MacAskill, Michael R. Anderson, Tim J. Çakmak, Yusuf O. Sensors (Basel) Article Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease. MDPI 2022-06-18 /pmc/articles/PMC9230824/ /pubmed/35746395 http://dx.doi.org/10.3390/s22124613 Text en © 2022 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
Khwaounjoo, Prashanna
Singh, Gurleen
Grenfell, Sophie
Özsoy, Burak
MacAskill, Michael R.
Anderson, Tim J.
Çakmak, Yusuf O.
Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title_full Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title_fullStr Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title_full_unstemmed Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title_short Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor
title_sort non-contact hand movement analysis for optimal configuration of smart sensors to capture parkinson’s disease hand tremor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230824/
https://www.ncbi.nlm.nih.gov/pubmed/35746395
http://dx.doi.org/10.3390/s22124613
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