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Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back

The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms f...

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Autores principales: Adamowicz, Lukas, Karahanoglu, F. Isik, Cicalo, Christopher, Zhang, Hao, Demanuele, Charmaine, Santamaria, Mar, Cai, Xuemei, Patel, Shyamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699326/
https://www.ncbi.nlm.nih.gov/pubmed/33228035
http://dx.doi.org/10.3390/s20226618
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author Adamowicz, Lukas
Karahanoglu, F. Isik
Cicalo, Christopher
Zhang, Hao
Demanuele, Charmaine
Santamaria, Mar
Cai, Xuemei
Patel, Shyamal
author_facet Adamowicz, Lukas
Karahanoglu, F. Isik
Cicalo, Christopher
Zhang, Hao
Demanuele, Charmaine
Santamaria, Mar
Cai, Xuemei
Patel, Shyamal
author_sort Adamowicz, Lukas
collection PubMed
description The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson’s disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: [Formula: see text] vs. [Formula: see text] in healthy adults) and a previously published algorithm (precision: [Formula: see text] vs. [Formula: see text] in persons with Parkinson’s disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features.
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spelling pubmed-76993262020-11-29 Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back Adamowicz, Lukas Karahanoglu, F. Isik Cicalo, Christopher Zhang, Hao Demanuele, Charmaine Santamaria, Mar Cai, Xuemei Patel, Shyamal Sensors (Basel) Article The ability to perform sit-to-stand (STS) transfers has a significant impact on the functional mobility of an individual. Wearable technology has the potential to enable the objective, long-term monitoring of STS transfers during daily life. However, despite several recent efforts, most algorithms for detecting STS transfers rely on multiple sensing modalities or device locations and have predominantly been used for assessment during the performance of prescribed tasks in a lab setting. A novel wavelet-based algorithm for detecting STS transfers from data recorded using an accelerometer on the lower back is presented herein. The proposed algorithm is independent of device orientation and was validated on data captured in the lab from younger and older healthy adults as well as in people with Parkinson’s disease (PwPD). The algorithm was then used for processing data captured in free-living conditions to assess the ability of multiple features extracted from STS transfers to detect age-related group differences and assess the impact of monitoring duration on the reliability of measurements. The results show that performance of the proposed algorithm was comparable or significantly better than that of a commercially available system (precision: [Formula: see text] vs. [Formula: see text] in healthy adults) and a previously published algorithm (precision: [Formula: see text] vs. [Formula: see text] in persons with Parkinson’s disease). Moreover, features extracted from STS transfers at home were able to detect age-related group differences at a higher level of significance compared to data captured in the lab during the performance of prescribed tasks. Finally, simulation results showed that a monitoring duration of 3 days was sufficient to achieve good reliability for measurement of STS features. These results point towards the feasibility of using a single accelerometer on the lower back for detection and assessment of STS transfers during daily life. Future work in different patient populations is needed to evaluate the performance of the proposed algorithm, as well as assess the sensitivity and reliability of the STS features. MDPI 2020-11-19 /pmc/articles/PMC7699326/ /pubmed/33228035 http://dx.doi.org/10.3390/s20226618 Text en © 2020 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
Adamowicz, Lukas
Karahanoglu, F. Isik
Cicalo, Christopher
Zhang, Hao
Demanuele, Charmaine
Santamaria, Mar
Cai, Xuemei
Patel, Shyamal
Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title_full Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title_fullStr Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title_full_unstemmed Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title_short Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back
title_sort assessment of sit-to-stand transfers during daily life using an accelerometer on the lower back
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699326/
https://www.ncbi.nlm.nih.gov/pubmed/33228035
http://dx.doi.org/10.3390/s20226618
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