Indoor Mobility Patterns Measured by PIR Sensors for Classifying Health-Related Quality of Life in Older Adults Using Machine Learning

Passive infrared (PIR) sensors provide a low-cost, unobtrusive, and privacy-preserving approach for continuously monitoring daily activity in older adults. This study investigated whether indoor mobility features derived from PIR sensors could discriminate levels of health-related quality of life (HRQoL) in community-dwelling older adults living alone. Mobility variables were extracted from three months of PIR sensor recordings and aggregated at the participant level for 40 individuals, who were classified into high- and low-HRQoL groups according to the EQ-5D index. A nested stratified five-fold cross-validation framework was implemented, incorporating RandomOverSampler exclusively within the training folds to address class imbalance while preserving the original distribution of the outer test folds. Three machine learning classifiers—Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN)—were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). The SVM achieved the best overall performance, with an accuracy of 0.825±0.143, precision of 0.860±0.080, recall of 0.900±0.149, F1-score of 0.876±0.102, and AUC of 0.937±0.069. Random Forest achieved a comparable AUC of 0.933±0.109, whereas KNN showed lower overall performance and greater variability across the outer folds. Aggregated out-of-fold predictions further provided class-specific performance estimates while preserving the original participant distribution, confirming that model evaluation was conducted exclusively on non-oversampled test data. Overall, the findings support the feasibility of combining PIR-derived mobility features with interpretable machine learning models to investigate HRQoL in older adults. These results should be considered proof-of-feasibility and warrant validation in larger, independent, and more diverse cohorts.