Daily fall risk assessment for older adults using a balance sensor with machine learning.

Introduction: Fall-risk assessment is important for preventing falls in older adults, but many conventional balance tests are time-consuming and difficult to use frequently in daily practice. This study aimed to evaluate a rapid 30-s quiet-standing assessment using a balance sensor combined with machine learning for fall-history classification and fall-risk assessment. Methods: A total of 394 older adults (mean age 71.56 ± 6.69 years) participated, including 91 individuals in a laboratory cohort, 291 in an independent hospital cohort, and 12 in a 10-day home-based feasibility study. The balance sensor captured plantar pressure-distribution images during quiet standing. From these recordings, center-of-pressure (CoP) trajectories and model-derived center-of-gravity (CoG) descriptors were obtained. Spatial, stability, frequency, temporal, and statistical features were extracted, and multiple machine-learning models were evaluated. Model development in the laboratory cohort used nested stratified cross-validation, and the final pipeline was externally validated in the hospital cohort. Results: In the laboratory cohort, the highest area under the receiver operating characteristic curve (AUC) was 0.71, achieved by the random forest model under the eyes-open condition. Under the eyes-closed condition, the highest AUC was 0.68. In the independent hospital cohort, external validation of the final laboratory-developed model yielded an AUC of 0.68 and an accuracy of 72.0%, which was numerically higher than those of the conventional functional assessments evaluated in this study. The home-based component supported the feasibility of repeated short-duration balance measurement outside the laboratory. Discussion: These findings suggest that a brief sensor-based standing assessment combined with machine learning may provide a practical approach for fall-history classification and fall-risk assessment in older adults. However, the main analyses were based on retrospective fall history or documented clinical fall risk rather than prospectively observed future falls. Larger prospective and multi-center studies are needed to determine predictive validity and broader clinical utility.