Fig. 3

Model predictions and feature selection. Predictions of patient recovery, in terms of changes in velocity coverage, using multiple regression analysis. Each gray dot represents a single repeat of a cross-validation staggered for easy visualization by fitting a probability density function. Each black dot and bar represents the mean R² ± SD. Positive (concentric) and negative (eccentric) work features are indicated in red and blue, respectively. The negative work in shoulder adduction and positive work in elbow flexion and extension features were selected most often by the LASSO model across the cross-validation repeats. The successive removal of the four most selected features resulted in a diminishing return of model accuracy. The full model equation is represented as y = [4.85A + 1.46B + 2.75C - 0.41D + 0.16E + 0.09F + 211.0] × 10^− 3, where model coefficients assigned to each feature were averaged across cross-validation repeats