Fig. 2
From: Wearable airbag technology and machine learned models to mitigate falls after stroke
Pre-impact fall detection performance of post-stroke individuals’ falls tested on control- and stroke-trained models. Average performance metrics of recall, precision, F1-score, and AUC for pre-impact fall detection of post-stroke individuals’ falls using an AdaBoost Classifier trained on control (blue) or stroke (red) cohort data. Each model is stratified and compared across different fall types: (A) all falls, (B) lateral falls, (C) AP falls using paired t tests. Tests that resulted in a statistically significant difference (as defined by P < 0.05) are indicated by a single *. Tests that are significant after compensating for the family-wise error rate of using repeated t tests for all three fall types, as defined by the Holm-Bonferroni method, are marked with **