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Article: Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation

TitleAutomatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation
Authors
KeywordsActivity recognition
Automatic framework
Balance
Community-dwelling elderly
Fall risk
Issue Date2021
Citation
Journal of Medical Internet Research, 2021, v. 23, n. 12, article no. e30135 How to Cite?
AbstractBackground: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.
Persistent Identifierhttp://hdl.handle.net/10722/336295
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHsu, Yu Cheng-
dc.contributor.authorWang, Hailiang-
dc.contributor.authorZhao, Yang-
dc.contributor.authorChen, Frank-
dc.contributor.authorTsui, Kwok Leung-
dc.date.accessioned2024-01-15T08:25:17Z-
dc.date.available2024-01-15T08:25:17Z-
dc.date.issued2021-
dc.identifier.citationJournal of Medical Internet Research, 2021, v. 23, n. 12, article no. e30135-
dc.identifier.urihttp://hdl.handle.net/10722/336295-
dc.description.abstractBackground: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.-
dc.languageeng-
dc.relation.ispartofJournal of Medical Internet Research-
dc.subjectActivity recognition-
dc.subjectAutomatic framework-
dc.subjectBalance-
dc.subjectCommunity-dwelling elderly-
dc.subjectFall risk-
dc.titleAutomatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.2196/30135-
dc.identifier.pmid34932008-
dc.identifier.scopuseid_2-s2.0-85121980491-
dc.identifier.volume23-
dc.identifier.issue12-
dc.identifier.spagearticle no. e30135-
dc.identifier.epagearticle no. e30135-
dc.identifier.eissn1438-8871-
dc.identifier.isiWOS:000740350700007-

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