Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs

TL;DR

This paper introduces a novel Bayesian graph-based method to model and assess individual patient recovery trajectories in physical therapy, enabling personalized feedback and optimized exercise recommendations.

Contribution

It presents a new approach using probabilistic graph distances between learned joint-movement graphs to quantify recovery progress and guide therapy.

Findings

Effective modeling of recovery trajectories using Bayesian graph distances.

Ability to recommend personalized exercise routines based on impairment level.

Demonstrated potential for improved rehabilitation feedback.

Abstract

Patients who are undergoing physical rehabilitation, benefit from feedback that follows from reliable assessment of their cumulative performance attained at a given time. In this paper, we provide a method for the learning of the recovery trajectory of an individual patient, as they undertake exercises as part of their physical therapy towards recovery of their loss of movement ability, following a critical illness. The difference between the Movement Recovery Scores (MRSs) attained by a patient, when undertaking a given exercise routine on successive instances, is given by a statistical distance/divergence between the (posterior) probabilities of random graphs that are Bayesianly learnt using time series data on locations of 20 of the patient's joints, recorded on an e-platform as the patient exercises. This allows for the computation of the MRS on every occasion the patient undertakes this exercise, using which, the recovery trajectory is drawn. We learn each graph as a Random Geometric Graph drawn in a probabilistic metric space, and identify the closed-form marginal posterior of any edge of the graph, given the correlation structure of the multivariate time series data on joint locations. On the basis of our recovery learning, we offer recommendations on the optimal exercise routines for patients with given level of mobility impairment.