Wearable airbag technology and machine learned models to mitigate falls after stroke

Table 2 Effect of non-fall type (ADLs or near-falls) on pre-impact fall detection for stroke- and control-trained models

	Recall		Precision		\({\mathbf{F}}_{1}\)-Score		AUC
	M	SD	M	SD	M	SD	M	SD
Stroke-trained model
All falls
ADL	0.95	0.07	0.94	0.06	0.94	0.05	0.97	0.04
Near-fall	0.93	0.07	0.92	0.08	0.92	0.06	0.92	0.08
P	0.53		0.3		0.27		0.02*
Lateral falls
ADL	0.93	0.08	0.96	0.06	0.94	0.06	0.97	0.05
Near-Fall	0.92	0.08	0.93	0.10	0.92	0.07	0.95	0.05
P	0.73		0.28		0.33		0.19
AP Falls
ADL	0.79	0.23	0.84	0.20	0.79	0.19	0.97	0.05
Near-Fall	0.80	0.23	0.89	0.19	0.82	0.19	0.88	0.16
P	0.81		0.44		0.65		0.03*
Control-trained model
All falls
ADL	0.88	0.12	0.92	0.07	0.89	0.08	0.94	0.08
Near-fall	0.85	0.10	0.96	0.06	0.90	0.08	0.91	0.13
P	0.42		0.07		0.85		0.37
Lateral falls
ADL	0.91	0.13	0.97	0.04	0.94	0.08	0.98	0.04
Near-fall	0.89	0.07	0.95	0.08	0.92	0.06	0.96	0.05
P	0.56		0.25		0.45		0.15
AP falls
ADL	0.75	0.22	0.82	0.25	0.76	0.21	0.94	0.07
Near-Fall	0.49	0.34	0.76	0.40	0.57	0.34	0.86	0.15
P	0.01**		0.58		0.04*		0.04*

Average recall, precision, \({\mathrm{F}}_{1}\)-score, and AUC reported for stroke-trained (top) and control-trained (bottom) models to compare the effect of non-fall type (ADL, near-fall) on model performance for each category of fall types (all, lateral, AP). For each fall type, P values are reported to compare the significance when using ADLs versus near-falls for the non-falls type in the model. Tests that resulted in a statistically significant value (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 are marked with **

ISSN: 1743-0003