Fig. 3
From: Wearable airbag technology and machine learned models to mitigate falls after stroke
The effect of an increasing quantity of test subjects with severe stroke-related impairments on pre fall detection. Average recall, precision, F1-score, and AUC are displayed for unique, randomly selected subgroups of stroke individuals (n = 5 per subgroup) tested on both control- and stroke-trained models. Performance is stratified by the quantity of stroke individuals with severe stroke-related impairments (i.e. unstable ambulators) included in the test set. For zero to three unstable ambulators in a test set, 100 unique subgroups were input into both models. For four or five unstable ambulators in a test set, the maximum number of unique possible subgroups were used, i.e. 75 and 1 subgroup(s), respectively. Visual trends are displayed to demonstrate how an increase in the severity of the test group impacts model performance for each subcategory of fall types: (A) all falls, (B) lateral falls, (C) AP falls. Notably, control-trained model performance generally declines with an increase in unstable ambulators tested upon, while stroke-trained models are unaffected or even result in an improved performance with an increased number of unstable ambulators tested upon