Application of machine learning for detecting high fall risk in middle aged workers using video-based analysis of the first three steps.

Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.

Methods: Train data (n=190, age 54.5±7.7 years, 48.9% male) and validation data (n=28, age 52.3±6.0 years, 53.6% male) were enrolled in this study. Pose estimation was performed using a marker-free deep pose estimation method called MediaPipe Pose. The first three steps, including the movements of the arms, legs, trunk, and pelvis, were recorded using an RGB camera, and the gait features were identified. Using these gait features and fall histories, a stratified K-fold was used to ensure balanced training and test data, and the area under the curve (AUC) and 95% confidence interval (CI) were calculated.

Results: Of 77 gait features in the first three steps, we found 3 gait features in men with an AUC of 0.909 (95% CI, 0.879-0.939) for fall risk, indicating an 'Excellent' (0.9-1.0) classification, while we determined 5 gait features in women with an AUC of 0.670 (95% CI, 0.621-0.719), indicating a 'sufficient' (0.6-0.7) classification.

Conclusions: These findings suggest that fall risk prediction can be developed based on ML and the first three steps in men; however, the accuracy was only sufficient in men. Further development of the formula is required for women to improve its accuracy in the middle-aged working population.