Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults
This study presents a novel framework that utilizes instrumented footwear to predict fall risk in institutionalized older adults by leveraging stride-to-stride gait data. The older adults are categorized into fallers and non-fallers using three distinct criteria: retrospective fall history, prospect...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10772477/ |
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| author | Huanghe Zhang Chuanyan Wu Yulong Huang Rui Song Damiano Zanotto Sunil K. Agrawal |
| author_facet | Huanghe Zhang Chuanyan Wu Yulong Huang Rui Song Damiano Zanotto Sunil K. Agrawal |
| author_sort | Huanghe Zhang |
| collection | DOAJ |
| description | This study presents a novel framework that utilizes instrumented footwear to predict fall risk in institutionalized older adults by leveraging stride-to-stride gait data. The older adults are categorized into fallers and non-fallers using three distinct criteria: retrospective fall history, prospective fall occurrence, and a combination of both retrospective and prospective data. Three types of data collected from N=95 institutionalized older adults are analyzed: traditional timed mobility tests, gait data collected from a validated electronic walkway, and gait data collected with instrumented footwear developed by our team. The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. The models are evaluated using leave-one-out cross-validation and 10-fold cross-validation. The results show that models using gait data from the instrumented footwear outperformed those based on traditional tests and walkway data, with area under the receiver operating characteristic curve (AUC) values for predicting prospective falls being 0.47, 0.66, and 0.80, respectively. The sensitivity of the models increases when they are trained using both past and future falls data, rather than relying solely on past or future falls data. This study demonstrates the potential of instrumented footwear for fall risk assessment in elderly individuals. The findings provide valuable insights for fall prevention and care, highlighting the superior predictive capabilities of the developed system compared to traditional methods. |
| format | Article |
| id | doaj-art-1be67656a7254532bbabeae2089c3f67 |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-1be67656a7254532bbabeae2089c3f672025-08-20T01:59:13ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102024-01-01324260426910.1109/TNSRE.2024.351030010772477Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older AdultsHuanghe Zhang0https://orcid.org/0000-0003-2770-8335Chuanyan Wu1https://orcid.org/0000-0003-2705-866XYulong Huang2https://orcid.org/0000-0001-9303-9083Rui Song3https://orcid.org/0000-0002-4119-4433Damiano Zanotto4https://orcid.org/0000-0003-3514-6889Sunil K. Agrawal5https://orcid.org/0000-0002-4008-1437Center for Robotics, School of Control Science and Engineering, Shandong University, Jinan, Shandong, ChinaCenter for Robotics, School of Control Science and Engineering, Shandong University, Jinan, Shandong, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, ChinaCenter for Robotics, School of Control Science and Engineering, Shandong University, Jinan, Shandong, ChinaDepartment of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ, USADepartment of Mechanical Engineering, Columbia University, New York, NY, USAThis study presents a novel framework that utilizes instrumented footwear to predict fall risk in institutionalized older adults by leveraging stride-to-stride gait data. The older adults are categorized into fallers and non-fallers using three distinct criteria: retrospective fall history, prospective fall occurrence, and a combination of both retrospective and prospective data. Three types of data collected from N=95 institutionalized older adults are analyzed: traditional timed mobility tests, gait data collected from a validated electronic walkway, and gait data collected with instrumented footwear developed by our team. The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. The models are evaluated using leave-one-out cross-validation and 10-fold cross-validation. The results show that models using gait data from the instrumented footwear outperformed those based on traditional tests and walkway data, with area under the receiver operating characteristic curve (AUC) values for predicting prospective falls being 0.47, 0.66, and 0.80, respectively. The sensitivity of the models increases when they are trained using both past and future falls data, rather than relying solely on past or future falls data. This study demonstrates the potential of instrumented footwear for fall risk assessment in elderly individuals. The findings provide valuable insights for fall prevention and care, highlighting the superior predictive capabilities of the developed system compared to traditional methods.https://ieeexplore.ieee.org/document/10772477/Fall risk assessmentinstrumented footwearmachine learning |
| spellingShingle | Huanghe Zhang Chuanyan Wu Yulong Huang Rui Song Damiano Zanotto Sunil K. Agrawal Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults IEEE Transactions on Neural Systems and Rehabilitation Engineering Fall risk assessment instrumented footwear machine learning |
| title | Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults |
| title_full | Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults |
| title_fullStr | Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults |
| title_full_unstemmed | Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults |
| title_short | Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults |
| title_sort | fall risk prediction using instrumented footwear in institutionalized older adults |
| topic | Fall risk assessment instrumented footwear machine learning |
| url | https://ieeexplore.ieee.org/document/10772477/ |
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