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

Autor: Olivia K. Botonis, Yaar Harari, Kyle R. Embry, Chaithanya K. Mummidisetty, David Riopelle, Matt Giffhorn, Mark V. Albert, Vallery Heike, Arun Jayaraman
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of NeuroEngineering and Rehabilitation, Vol 19, Iss 1, Pp 1-14 (2022)
Druh dokumentu: article
ISSN: 1743-0003
DOI: 10.1186/s12984-022-01040-4
Popis: Abstract Background Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments. To address this gap, we investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. Methods We collected data from a wearable airbag’s inertial measurement units (IMUs) from individuals with (n = 20 stroke) and without (n = 15 control) history of stroke while performing a series of falls (842 falls total) and non-falls (961 non-falls total) in a laboratory setting. A leave-one-subject-out crossvalidation was used to compare the performance of two identical machine learned models (adaptive boosting classifier) trained on cohort-dependent data (control or stroke) to pre-detect falls in the stroke cohort. Results The average performance of the model trained on stroke data (recall = 0.905, precision = 0.900) had statistically significantly better recall (P = 0.0035) than the model trained on control data (recall = 0.800, precision = 0.944), while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior–posterior (AP) falls (stroke-trained model’s F1-score was 35% higher, P = 0.019). Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the AUC (Area under the receiver operating characteristic) for classifying AP falls for both models (P
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