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Background <p>With global aging, falls have become a major threat to geriatric health. Traditional clinical assessments often suffer from a “ceiling effect,” failing to detect subtle motor impairments in high-functioning older adults. Moreover, steady-state gait analysis lacks ecological validity in capturing dynamic stability during complex activities. To address these gaps, this study utilizes a dual-task paradigm and machine learning to develop a multidimensional, high-fidelity fall-risk classification framework.</p> Methods <p>A retrospective case-control study was conducted involving 209 community-dwelling older adults (50 fallers and 159 non-fallers). A multi-dimensional feature set was established, comprising demographics, anthropometrics, physical activity, and a battery of clinical functional assessments. An optical motion capture system was utilized to extract spatiotemporal gait parameters during a dual-task paradigm involving surface perturbations and a modified Stroop task. Eight machine learning algorithms, including Naive Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed and validated to identify fall risk.</p> Results <p>The machine learning models demonstrated robust and competitive performance, with the top models achieving high discriminatory accuracy (up to 0.879) and excellent area under the curve (AUC up to 0.927) on the test set. Ten key predictors were identified, with dual-task gait variability—specifically the CV of cycle time (SHAP value = 0.083)—contributing most significantly to the screening framework. While traditional functional scales showed a “ceiling effect” with limited discriminative power, dual-task gait parameters effectively unmasked latent motor instabilities. SHAP analysis further demonstrated that the NB model provided highly interpretable, continuous probability scores for individual fall risk screening.</p> Conclusions <p>Combining dual-task gait variability with clinical functional assessments via machine learning algorithms provides a robust and interpretable framework for faller identification. The integration of optical gait analysis and machine learning facilitates early risk screening and personalized retrospective evaluation, offering a promising tool to assist in detecting older adults at risk for fall-related injuries in the aging population.</p>

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Machine learning-based fall risk prediction in community-dwelling older adults using dual-task spatiotemporal gait parameters

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Gengxin Dong,
  • Haizhou Hu,
  • Yuxin Guo,
  • Qi Gao,
  • Dapeng Bao

摘要

Background

With global aging, falls have become a major threat to geriatric health. Traditional clinical assessments often suffer from a “ceiling effect,” failing to detect subtle motor impairments in high-functioning older adults. Moreover, steady-state gait analysis lacks ecological validity in capturing dynamic stability during complex activities. To address these gaps, this study utilizes a dual-task paradigm and machine learning to develop a multidimensional, high-fidelity fall-risk classification framework.

Methods

A retrospective case-control study was conducted involving 209 community-dwelling older adults (50 fallers and 159 non-fallers). A multi-dimensional feature set was established, comprising demographics, anthropometrics, physical activity, and a battery of clinical functional assessments. An optical motion capture system was utilized to extract spatiotemporal gait parameters during a dual-task paradigm involving surface perturbations and a modified Stroop task. Eight machine learning algorithms, including Naive Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed and validated to identify fall risk.

Results

The machine learning models demonstrated robust and competitive performance, with the top models achieving high discriminatory accuracy (up to 0.879) and excellent area under the curve (AUC up to 0.927) on the test set. Ten key predictors were identified, with dual-task gait variability—specifically the CV of cycle time (SHAP value = 0.083)—contributing most significantly to the screening framework. While traditional functional scales showed a “ceiling effect” with limited discriminative power, dual-task gait parameters effectively unmasked latent motor instabilities. SHAP analysis further demonstrated that the NB model provided highly interpretable, continuous probability scores for individual fall risk screening.

Conclusions

Combining dual-task gait variability with clinical functional assessments via machine learning algorithms provides a robust and interpretable framework for faller identification. The integration of optical gait analysis and machine learning facilitates early risk screening and personalized retrospective evaluation, offering a promising tool to assist in detecting older adults at risk for fall-related injuries in the aging population.