Individualised recovery trajectories of patients with impeded mobility, using distance between probability distributions of learnt graphs
This work addresses the need for reliable, individualized feedback in physical therapy for patients with mobility impairments, offering a novel computational approach to track recovery progress.
The paper tackles the problem of assessing recovery in patients undergoing physical rehabilitation by learning individual recovery trajectories using movement data from 20 joints. The result is a method that computes Movement Recovery Scores based on statistical distances between Bayesianly learnt random graphs, enabling recommendations for optimal exercise routines.
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.