Decision support framework for predicting rate of gait recovery with optimized treatment planning

Rate of Recovery during the rehabilitation procedure is not effectively evaluated due to existing limitations of measurement tools and large human variability. The conventional way to treat lower-limb injuries is by conducting a physician-guided rehabilitation process that could take several weeks or months. But the process of estimating the rate of recovery is entirely based on the expertise of a therapist. Moreover, it remains unclear to a large extent about the factors have contributed to the success of treatment, the most effective rehabilitation duration, and recovery progression rate. To address these limitations, a random subspace ensemble k-nearest neighbor (RSEnkNN) model is proposed to predict the rehabilitation duration of an individual after initial baseline assessment of gait trajectories. The model is trained using an open-source dataset comprising 211 healthy and 2084 patients with functional gait disorders. The proposed model is able to predict rehabilitation duration with an accuracy of 88-89 % in case of ankle, calcaneus, and hip injury while the success rate of 82 % is achieved for knee disorders. Features computed from ground reaction force, measures of postural steadiness, and bilateral symmetry turn out to be effective in predicting the early, moderate, or late recovery of an individual. The significantly contributing features for each anomaly class are reported using recursive feature elimination (RFE) technique to support clinicians. Thereafter, recovery rate is determined using a novel metric Rate of Gait Recovery (RoGR) index that could be effective for treatment planning and re-scheduling of therapy sessions (if required). This work determined that the most effective rehabilitation period is 2-6 weeks post-surgery for restoration to normal walk. Thus, a prediction tool for strengthening clinical decision-making is proposed to support early rehabilitation.

Automated summary

This study proposes a model, RSEnkNN, to predict the rehabilitation duration of an individual based on the initial baseline assessment of gait trajectories. The model achieved an accuracy of 88-89% for ankle, calcaneus, and hip injury prediction, and 82% for knee disorders. Features computed from ground reaction force, measures of postural steadiness, and bilateral symmetry were found to be effective in predicting the recovery progression of an individual. The study also introduces the Rate of Gait Recovery (RoGR) index for treatment planning and scheduling.