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Predicting Multiple Clinical Outcomes Related to Functional Recovery and Social Isolation Among Older Adults After Lower-Limb Fracture or Hip Replacement

Santosh Ray, Pratik K. Mishra, Ali Abedi, Charlene H. Chu, Amir Ahmad, Shehroz S. Khan

arXiv:2608.23531v1cs.CVcs.LG

TL;DR

Older adults recovering after lower-limb surgery may face intertwined mobility and social-engagement challenges that are not routinely assessed together. This study formulates simultaneous prediction of five clinical scores from multimodal sensor data as multi-output regression and finds joint deep-learning models outperform separate and traditional approaches.

  • Problem

    Recovery can involve both mobility and social-engagement limitations, while regular assessment of these dimensions is mostly not covered by primary care services.

    Source: §I · pp. 1–2

  • Method

    Using daily multimodal sensor features from 18 participants monitored over 1,008 days, the study compares traditional machine-learning and tabular deep-learning single- and multi-output regressors for five clinical measures.

    Source: §I · p. 2, A. MAISON-LLF DATASET · p. 4, B. EXPERIMENTAL SETUP · p. 4

  • Results

    Multi-output regressors performed better than single-output regressors across tested machine-learning and deep-learning approaches, with NODE emerging as the best multi-output regressor.

    Source: §I · p. 2, B. LEAVE-ONE-PERSON-OUT CROSS-VALIDATION · p. 8, C. LEAVE-ONE-WEEK-OUT CROSS-VALIDATION · p. 10

  • Takeaways & Limitations

    Feature importance suggests that mobility and physiological data are important for estimating functional recovery and social isolation jointly.

    Source: §I · p. 2, §V · p. 15

    The study analyzed multimodal sensor data from only 18 patients, with a female-dominated cohort and a single geographic region that included potentially confounding seasons.

    Source: §V · p. 15

Abstract

from arXiv · show

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.

I. INTRODUCTION

Recovery after lower-limb fracture or hip replacement can affect mobility and social engagement, but these dimensions are rarely assessed together. This study formulates simultaneous prediction of five clinical measures from multimodal sensor data.

  • Community recovery can involve limitations in both mobility and social engagement, while routine primary care often does not regularly assess both dimensions.Source: §I · p. 1
  • Mobility, sleep, heart rate, and other physiological indicators have been separately associated with functional mobility and social isolation.Source: §I · p. 1
  • Multimodal data spanning indoor motion, outdoor movement, sleep, heart rate, and steps can provide an overall profile for predicting functional decline and social isolation.Source: §I · p. 2
  • The MAISON-LLF dataset contains longitudinal wearable, ambient, sleep, mobility, and clinical assessment data from older adults recovering in the community.Source: §I · p. 2
  • The study formulates prediction of OHS, OKS, TUG, Chair Stand, and SIS as a multi-output regression problem to model these outcomes jointly.Source: §I · p. 2
  • Tabular deep learning models were investigated, while feature selection and SHAP analysis examined whether multimodal sensor variables were useful for prediction.Source: §I · p. 2

II. RELATED WORK

Prior work has used sensor-based machine learning to monitor social, functional, and rehabilitation-related outcomes in older adults. Studies specifically addressing lower-limb fracture recovery after discharge remain limited.

B. MULTI-OUTPUT AND MULTI-TASK LEARNING IN HEALTH-RELATED PREDICTION

Multi-output and multi-task learning have been used for related health, biomedical, and sensor-prediction problems, but post-discharge recovery after lower-limb fracture remains underexplored. The paper addresses this gap by jointly modelling functional and social recovery outcomes.

III. METHODS

The study uses longitudinal multimodal sensor and clinical data from community-dwelling older adults recovering after lower-limb fracture or hip replacement. Daily sensor features were linked to repeated clinical assessments over an eight-week monitoring period.

  • The MAISON-LLF dataset followed older adults from shortly after discharge for up to eight weeks using longitudinal multimodal sensor and clinical assessment data.Source: A. MAISON-LLF DATASET · p. 4
  • Clinical assessments included hip and knee pain/function scores, functional mobility and balance, and lower-extremity strength and capacity.Source: A. MAISON-LLF DATASET · p. 4
  • 1,008 days of multimodal monitoring came from 18 participants with daily sensor-derived features and repeated questionnaire and physical-performance assessments.Source: A. MAISON-LLF DATASET · p. 4
  • The cohort had a mean age of 76.5 years, included 14 female participants and 14 participants living alone, and represented hip replacement and several fracture types.Source: A. MAISON-LLF DATASET · p. 4

B. EXPERIMENTAL SETUP

The study compares traditional machine-learning and tabular deep-learning regressors for multimodal clinical-score prediction, using participant- and week-based validation to assess generalization.

  • The evaluation included LR, ET, RF, KNN, SVR, NODE, FT-Transformer, TabPFN, and TabNet regressors.Source: B. EXPERIMENTAL SETUP · p. 4
  • Models used min-max scaling to align target ranges before training and inverse transformation before computing MSE and MAE.Source: B. EXPERIMENTAL SETUP · p. 5
  • Leave-one-person-out cross-validation tested generalization to completely unseen older adults without prior exposure to their baseline behaviours.Source: B. EXPERIMENTAL SETUP · p. 5
  • Leave-one-week-out cross-validation tested robustness to week-to-week behavioural shifts across the cohort.Source: B. EXPERIMENTAL SETUP · p. 5

A. CORRELATIONS AMONG CLINICAL OUTCOMES

Clinical outcomes showed coupled relationships between functional mobility and social isolation, with key correlations remaining stable across unseen participants.

B. LEAVE-ONE-PERSON-OUT CROSS-VALIDATION

Leave-one-person-out validation showed that multi-output prediction generally outperformed single-output prediction, with the strongest gains from tabular deep-learning models, especially NODE.

C. LEAVE-ONE-WEEK-OUT CROSS-VALIDATION

Leave-one-week-out validation tested whether models generalized across unseen temporal behavior. Multi-output tabular deep learning models generally benefited most from jointly predicting the five clinical outcomes.

D. FEATURES DISTRIBUTION ANALYSIS

Sensor features showed strongly skewed distributions and many upper outliers across activity, mobility, heart-rate, and sleep modalities. These patterns reflect predominantly home-based behavior alongside episodic activity, exertion, or disrupted rest.

  • Acceleration and motion features were right-skewed, with dense upper outliers and long tails toward 1.0.The pattern is described as prolonged sedentary behavior punctuated by bursts of activity or nighttime restlessness.Source: D. FEATURES DISTRIBUTION ANALYSIS · p. 11
  • Step-count, step-mean, and step-max features displayed massive densities of upper outliers.Source: D. FEATURES DISTRIBUTION ANALYSIS · p. 11
  • Position-duration, position-distance-travelled, and position-count had medians near the distribution floor, with most variation appearing as extreme upper outliers.The cohort spent the vast majority of time at home.Source: D. FEATURES DISTRIBUTION ANALYSIS · p. 11
  • Heart-rate maxima showed upper outliers, while several sleep features had extreme upper-bound values representing episodic exertion or disrupted rest.Source: D. FEATURES DISTRIBUTION ANALYSIS · p. 11

E. FEATURES IMPORTANCE AND INTERPRETABILITY

SHAP analysis of NODE identified behavioral timing and sleep characteristics as more informative than raw activity volume. Several volumetric and outdoor-mobility measures ranked near the bottom, partly because outside movement was sparse.

V. CONCLUSION AND FUTURE WORK

The study found that multi-output regression outperformed single-output approaches, with deep learning and NODE performing best, while multimodal mobility and physiological features supported prediction of recovery and social isolation. Interpretation is constrained by the small, geographically limited cohort and repeated daily records derived from only 18 participants.

  • Multi-output regressors outperformed single-output regressors across the tested machine learning and deep learning approaches.Source: §V · p. 15
  • The dataset comprised 1,008 monitoring days from only 18 patients, limiting the independent sample size.Source: §V · p. 15
  • Future work includes longitudinal individual recovery profiles, sequential deep learning models, richer phenotype descriptions, and geographic or neighborhood context.Source: §V · p. 15

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