Application of machine learning for detecting high fall risk in middle-aged workers using video-based analysis of the first 3 steps.

OBJECTIVES

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 3 steps in middle-aged workers.

METHODS

Participants to provide training data (n = 190, mean [SD] 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 3 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 cross-validation method was used to ensure balanced training and test data, and the area under the curve (AUC) and 95% CI were calculated.

RESULTS

Of 77 gait features in the first 3 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, whereas 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 3 steps in men; however, the accuracy was only "sufficient" in women. Further development of the formula for women is required to improve its accuracy in the middle-aged working population.