Early identification of mild cognitive impairment (MCI), fall risk, and frailty is critical for preventing cognitive and functional decline in aging populations. Sensor-based motor assessments combined with machine learning (ML) provide promising approaches for detecting early functional changes. This study aimed to design and evaluate an explainable machine learning (ML) framework that integrates sensor-derived motor features with demographic and clinical data to identify early indicators of these conditions. Eighty-three community-dwelling adults (≥60 years) completed multimodal motor assessments using the Mizzou Point-of-Care Assessment System (MPASS), which captures synchronized gait, balance, and sit-to-stand performance. Sensor-derived motor features were extracted from these assessments and combined with demographic and clinical variables to develop predictive models using XGBoost and Decision Tree algorithms. In addition, a unified multilabel modeling framework using XGBoost, Decision Tree, and AdaBoost was implemented to simultaneously predict MCI, fall risk, and frailty. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP) to identify key motor-function predictors and enhance transparency in model decision-making. By integrating explainable ML with multimodal motor-function data, this framework provides a data-driven approach for detecting early functional changes associated with cognitive and physical decline. The findings highlight the potential of combining sensor-based motor assessments with interpretable ML techniques to support scalable and transparent early screening strategies in aging populations.
