Model-Space Regularization and Fully Interpretable Algorithms for Postural Control Quantification

As falls prevalence increases with the aging of the population, early detection of balance degradation is of great importance for efficient prevention and treatment. This work addresses the problem of quantifiying static balance with fully interpretable learning algorithms. Our approach relies on a heuristic based variant of the aggregation of weak classifiers constrained with a new model-space regularization combined with a family of interpretable features. In our experiments, these models outperforms their regular alternative, opening promising new research directions.