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

Good word salad but nothing specific on how to prevent falls or mitigate them.

 Didn't your hospital bring in this 7 years ago?

Hip protector saves you when you slip February 2015

 

Do you prefer your  hospital incompetence NOT KNOWING? OR NOT DOING?

I personally prefer massive perturbations as you walk  so you know the movement necessary to prevent falls. That would build your confidence in safely walking more that this airbag technology. But since I'm not medically trained and stroke-addled besides, you can't listen to me.

The latest here:

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

Journal of NeuroEngineering and Rehabilitation , Volume 19(60)

NARIC Accession Number: J89519.  What's this?
ISSN: 1743-0003.
Author(s): Botonis, Olivia K.; Harari, Yaar; Embry, Kyle R.; Mummidisetty, Chaithanya K.; Riopelle, David; Giffhorn, Matt; Albert, Mark V.; Heike, Vallery; Jayaraman, Arun.
Project Number: 90REGE0003.
Publication Year: 2022.
Number of Pages: 14.
Abstract: 

Study investigated whether population-specific training data and modeling parameters are required to pre-detect falls in a chronic stroke population. Data were collected from inertial measurement unit sensors on airbags worn by 20 individuals with and 15 without (controls) a 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 cross-validation 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. The average performance of the model trained on stroke data had statistically significantly better recall than the model trained on control data, while precision was not statistically significantly different. Stratifying models trained on specific fall types revealed differences in pre-detecting anterior–posterior (AP) falls. Using activities of daily living as non-falls training data (compared to near-falls) significantly increased the area under the receiver operating characteristic for classifying AP falls for both models. Preliminary analysis suggests that users with more severe stroke impairments benefit further from a stroke-trained model. The optimal lead time (time interval pre-impact to detect falls) differed between control- and stroke-trained models. These results demonstrate the importance of population sensitivity, non-falls data, and optimal lead time for machine learned pre-impact fall detection specific to stroke. Existing fall mitigation technologies should be challenged to include data of neurologically impaired individuals in model development to adequately detect falls in other high fall risk populations.
Descriptor Terms: BODY MOVEMENT, ELECTRONICS, EQUILIBRIUM, INJURIES, MEASUREMENTS, POSTURE, PREVENTION, STROKE.


Can this document be ordered through NARIC's document delivery service*?: Y.
Get this Document: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-022-01040-4.

Citation: Botonis, Olivia K., Harari, Yaar, Embry, Kyle R., Mummidisetty, Chaithanya K., Riopelle, David, Giffhorn, Matt, Albert, Mark V., Heike, Vallery, Jayaraman, Arun. (2022). Wearable airbag technology and machine learned models to mitigate falls after stroke.  Journal of NeuroEngineering and Rehabilitation , 19(60) Retrieved 9/27/2022, from REHABDATA database.