Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement
2026-08-24 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors studied how older adults recover after a lower-limb fracture or hip replacement by using sensors that track movement, heart rate, sleep, and social activity for up to eight weeks. They combined this sensor data with clinical tests done every two weeks to predict recovery outcomes. Their method used machine learning to predict multiple recovery scores at once, which worked better than predicting them separately. They also found that using different types of sensor data together helped improve the predictions. This approach could help monitor recovery and social engagement in older adults living at home.
lower-limb fracturehip replacementmulti-output regressionmachine learningdeep learningsensor dataclinical assessmentSHAP analysisfunctional recoverysocial isolation scale
Authors
Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan
Abstract
Older adults recovering after lower-limb fracture or hip replacement may experience complex recovery trajectories. Most of the time, these clinical aspects are studied in isolation, masking their joint impact on recovery. This study used the MAISON-LLF dataset, which contains multimodal sensor and clinical assessment data from 18 older adults recovering in the community after lower-limb fracture or hip replacement. Participants were monitored for up to eight weeks, corresponding to a maximum of 1,008 participant-days of sensor monitoring. Forty-six daily features were extracted from indoor motion, acceleration, step count, heart rate, out-of-home mobility, and sleep data. Five clinical outcomes were assessed every two weeks: the Social Isolation Scale, Oxford Hip Score, Oxford Knee Score, Timed Up and Go test, and 30-second Chair Stand test. We utilize an inherent relationship between multi-modal sensor data and different clinical scores and formulate it as a multi-output regression problem. We tested various machine learning and deep learning single- and multi-output regression algorithms to predict these scores simultaneously. The results showed that predicting clinical scores jointly was better than separately. The tabular DL multi-output regressor, NODE, gave a remarkable performance of MSE=3.96 and MAE=1.02 in comparison to other multi- and single-output regressors. The SHAP feature analysis further showed the importance of including multimodal sensors to provide a good estimate of patients' recovery trajectory. This work may support the simultaneous assessment of functional recovery and social engagement among community-dwelling older adults and ultimately help improve their care and quality of life.