Prediction of fall risk among community-dwelling older adults using a wearable system
Abstract Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to dete...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-00458-5 |
| _version_ | 1819114566885310464 |
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| author | Thurmon E. Lockhart Rahul Soangra Hyunsoo Yoon Teresa Wu Christopher W. Frames Raven Weaver Karen A. Roberto |
| author_facet | Thurmon E. Lockhart Rahul Soangra Hyunsoo Yoon Teresa Wu Christopher W. Frames Raven Weaver Karen A. Roberto |
| author_sort | Thurmon E. Lockhart |
| collection | DOAJ |
| description | Abstract Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls. |
| first_indexed | 2024-12-22T04:47:21Z |
| format | Article |
| id | doaj.art-dfdfe155fab04566890792d59f00c7ed |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-22T04:47:21Z |
| publishDate | 2021-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dfdfe155fab04566890792d59f00c7ed2022-12-21T18:38:35ZengNature PortfolioScientific Reports2045-23222021-10-0111111410.1038/s41598-021-00458-5Prediction of fall risk among community-dwelling older adults using a wearable systemThurmon E. Lockhart0Rahul Soangra1Hyunsoo Yoon2Teresa Wu3Christopher W. Frames4Raven Weaver5Karen A. Roberto6School of Biological and Health Systems Engineering, Arizona State UniversityCrean College of Health and Behavioral Sciences, Chapman UniversityIndustrial Engineering, Yonsei UniversitySchool of Computing, Informatics, Decision Systems Engineering, Arizona State UniversitySchool of Biological and Health Systems Engineering, Arizona State UniversityDepartment of Human Development, College of Agricultural, Human, and Natural Resource Sciences, Washington State UniversityCenter for Gerontology, Virginia Polytechnic Institute and State UniversityAbstract Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.https://doi.org/10.1038/s41598-021-00458-5 |
| spellingShingle | Thurmon E. Lockhart Rahul Soangra Hyunsoo Yoon Teresa Wu Christopher W. Frames Raven Weaver Karen A. Roberto Prediction of fall risk among community-dwelling older adults using a wearable system Scientific Reports |
| title | Prediction of fall risk among community-dwelling older adults using a wearable system |
| title_full | Prediction of fall risk among community-dwelling older adults using a wearable system |
| title_fullStr | Prediction of fall risk among community-dwelling older adults using a wearable system |
| title_full_unstemmed | Prediction of fall risk among community-dwelling older adults using a wearable system |
| title_short | Prediction of fall risk among community-dwelling older adults using a wearable system |
| title_sort | prediction of fall risk among community dwelling older adults using a wearable system |
| url | https://doi.org/10.1038/s41598-021-00458-5 |
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