Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach
Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5536 |
| _version_ | 1797592720503472128 |
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| author | Shuaijie Wang Tuan Khang Nguyen Tanvi Bhatt |
| author_facet | Shuaijie Wang Tuan Khang Nguyen Tanvi Bhatt |
| author_sort | Shuaijie Wang |
| collection | DOAJ |
| description | Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one’s regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (<i>n</i> = 192), falls with lowering strategy (L-fall, <i>n</i> = 84), and falls with elevating strategy (E-fall, <i>n</i> = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (<i>n</i> = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1–20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls. |
| first_indexed | 2024-03-11T01:57:09Z |
| format | Article |
| id | doaj.art-8842c6feda884d4cb3bd57c29a69259f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:09Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8842c6feda884d4cb3bd57c29a69259f2023-11-18T12:32:29ZengMDPI AGSensors1424-82202023-06-012312553610.3390/s23125536Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning ApproachShuaijie Wang0Tuan Khang Nguyen1Tanvi Bhatt2Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL 60612, USADepartment of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USADepartment of Physical Therapy, University of Illinois at Chicago, Chicago, IL 60612, USATrip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one’s regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (<i>n</i> = 192), falls with lowering strategy (L-fall, <i>n</i> = 84), and falls with elevating strategy (E-fall, <i>n</i> = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (<i>n</i> = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1–20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls.https://www.mdpi.com/1424-8220/23/12/5536tripfall assessmentensemble classificationgait characteristics |
| spellingShingle | Shuaijie Wang Tuan Khang Nguyen Tanvi Bhatt Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach Sensors trip fall assessment ensemble classification gait characteristics |
| title | Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach |
| title_full | Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach |
| title_fullStr | Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach |
| title_full_unstemmed | Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach |
| title_short | Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach |
| title_sort | trip related fall risk prediction based on gait pattern in healthy older adults a machine learning approach |
| topic | trip fall assessment ensemble classification gait characteristics |
| url | https://www.mdpi.com/1424-8220/23/12/5536 |
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