Fig. 4
From: Wearable airbag technology and machine learned models to mitigate falls after stroke
Pre-impact fall detection optimal lead time dependent on model and non-fall type. An AdaBoost Classifier trained on control or stroke data were used to classify AP falls against ADLs or near-falls for a test subgroup of unstable ambulatory subjects (n = 5). AUC-ROC curves are displayed for varied lead times (50, 100, 150, 200, 300, 400, 500 ms) for classifying AP falls from ADLs in the control-trained model (A) and stroke-trained model (B), and from near-falls in the control-trained model (C) and stroke-trained model (D). The control-trained model performs similarly to the stroke-trained model’s maximal performance with a ~ 50% lead time reduction