MSS-Former: Multiscale Skeletal Transformer for Intelligent Fall Risk Prediction in Older Adults

Fall, a leading cause of accidental death and injury in older adults aged 65 and above, has become a rapidly growing health concern in aging populations worldwide. Data-driven methods integrating depth imaging technology have received growing attention in automated fall risk assessment owing to their noninvasiveness and less dependence on healthcare professionals. However, most existing depth image data-based models neglect the inherent physiological and potential functional connections and lack sufficient real-world data validation. To fill the research gap, we developed a novel approach named multiscale skeletal transformer (MSS-Former), leveraging depth image technology and deep-learning models for effective fall risk prediction. Our contributions mainly consist of four parts. First, we introduced a multimodel output feature fusion transformer in fall risk prediction, enabling output merging and weighting from multiple model streams dynamically. Second, we developed an innovative scheme to construct interjoint skeletal topology, systematically focusing on joints' intrinsic physiological and potential functional connections. Third, we constructed a ResNet-FPN, greatly enhancing multiscale feature extraction capabilities. Fourth, we conducted a field study in a local hospital and performed a comprehensive validation of our developed approach. The comparison results show that our approach achieved outstanding predictive performance, surpassing state-of-the-art methods on the real-world data set, with accuracy, precision, recall, and F1 scores of 97.84%, 97.33%, 96.97%, and 96.92%, respectively. In practice, the proposed approach would be of great value in the timely identification for individuals at high fall risk and facilitate decision making to take appropriate interventions.