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Article: A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data from the Timed-Up-and-Go Test in a Community Setting

TitleA Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data from the Timed-Up-and-Go Test in a Community Setting
Authors
Keywordsaccelerometer
Berg Balance Scale
data mining
fall risk prediction
gyroscope
Stroke
time-up-and-go test
Issue Date2020
Citation
IEEE Sensors Journal, 2020, v. 20, n. 16, p. 9339-9350 How to Cite?
AbstractPost-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via inertial measurement unit data measured from a 3-meter timed-up-and-go test. This approach used sensor technology and was thus easy to implement, and allowed a quantitative analysis of both gait and balance. The results showed that elastic net logistic regression achieved the best performance with 85% accuracy and 88% area under the curve compared with support vector machine, least absolute shrinkage and selection operator (LASSO), and stepwise logistic regression. This paper provides a framework for using sensor-based features together with a feature-selection strategy for screening and predicting the fall risk of post-stroke patients in a convenient setup with high accuracy. The findings of this study will not only enable the assessment of fall risk among post-stroke patients in a cost-effective manner but also provide decision-making support for community care providers and medical professionals in the form of sensor-based data on gait performance.
Persistent Identifierhttp://hdl.handle.net/10722/336241
ISSN
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 1.084
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHsu, Yu Cheng-
dc.contributor.authorZhao, Yang-
dc.contributor.authorHuang, Kuang Hui-
dc.contributor.authorWu, Ya Ting-
dc.contributor.authorCabrera, Javier-
dc.contributor.authorSun, Tien Lung-
dc.contributor.authorTsui, Kwok Leung-
dc.date.accessioned2024-01-15T08:24:47Z-
dc.date.available2024-01-15T08:24:47Z-
dc.date.issued2020-
dc.identifier.citationIEEE Sensors Journal, 2020, v. 20, n. 16, p. 9339-9350-
dc.identifier.issn1530-437X-
dc.identifier.urihttp://hdl.handle.net/10722/336241-
dc.description.abstractPost-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via inertial measurement unit data measured from a 3-meter timed-up-and-go test. This approach used sensor technology and was thus easy to implement, and allowed a quantitative analysis of both gait and balance. The results showed that elastic net logistic regression achieved the best performance with 85% accuracy and 88% area under the curve compared with support vector machine, least absolute shrinkage and selection operator (LASSO), and stepwise logistic regression. This paper provides a framework for using sensor-based features together with a feature-selection strategy for screening and predicting the fall risk of post-stroke patients in a convenient setup with high accuracy. The findings of this study will not only enable the assessment of fall risk among post-stroke patients in a cost-effective manner but also provide decision-making support for community care providers and medical professionals in the form of sensor-based data on gait performance.-
dc.languageeng-
dc.relation.ispartofIEEE Sensors Journal-
dc.subjectaccelerometer-
dc.subjectBerg Balance Scale-
dc.subjectdata mining-
dc.subjectfall risk prediction-
dc.subjectgyroscope-
dc.subjectStroke-
dc.subjecttime-up-and-go test-
dc.titleA Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data from the Timed-Up-and-Go Test in a Community Setting-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JSEN.2020.2987623-
dc.identifier.scopuseid_2-s2.0-85088862609-
dc.identifier.volume20-
dc.identifier.issue16-
dc.identifier.spage9339-
dc.identifier.epage9350-
dc.identifier.eissn1558-1748-
dc.identifier.isiWOS:000550685000050-

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