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

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dc.contributor.authorHsu, Yu-Chengen
dc.contributor.authorWang, Hailiangen
dc.contributor.authorZhao, Yangen
dc.contributor.authorChen, Franken
dc.contributor.authorTsui, Kwok-Leungen
dc.date.accessioned2022-09-02T13:58:10Zen
dc.date.available2022-09-02T13:58:10Zen
dc.date.issued2021-12-20en
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 degrees, 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 degrees, 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.en
dc.description.notesThis work was partly supported by the CityU Provost Project (grant no. 9610406) and the National Natural Science Foundation of China (no. 71901188) . The authors wish to thank Welfare Enterprises Association for its support to the study.en
dc.description.sponsorshipCityU Provost Project [9610406]; National Natural Science Foundation of China [71901188]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.2196/30135en
dc.identifier.issue12en
dc.identifier.othere30135en
dc.identifier.pmid34932008en
dc.identifier.urihttp://hdl.handle.net/10919/111694en
dc.identifier.volume23en
dc.language.isoenen
dc.publisherJMIR Publicationsen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectfall risken
dc.subjectbalanceen
dc.subjectactivity recognitionen
dc.subjectautomatic frameworken
dc.subjectcommunity-dwelling elderlyen
dc.titleAutomatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validationen
dc.title.serialJournal of Medical Internet Researchen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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