Machine learning model predicts fall risk for

picture: The Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test App was used to gather information on this analysis. Left: A participant completes a stroll check with a smartphone hooked up to the decrease again. Right: User interface of TOHRC Walk Test App after the stroll check is full.
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Credit: Juneau P, et al., 2022, PLOS Digital Health, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

In your protection, please use this URL to supply entry to the freely accessible article in PLOS Digital Health: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000088 

Article Title: Automated step detection with 6-minute stroll check smartphone sensors indicators for fall risk classification in decrease limb amputees

Author Countries: Canada, Slovenia

Funding: This analysis was funded by Natural Sciences and Engineering Research Council of Canada (NSERC). NSERC CREATE READI: RGPIN-2019-04106, E. D. L., https://carleton.ca/readi/ NSERC CREATE BEST 482728-2016-CREAT, N. B., http://create-best.com/#focus The funders had no function in research design, information assortment and evaluation, resolution to publish, or preparation of the manuscript.

Journal
PLOS Digital Health

Method of Research
Observational research

Subject of Research
People

Article Title
Automated step detection with 6-minute stroll check smartphone sensors indicators for fall risk classificiation in decrease limb amputees

COI Statement
Competing pursuits: The authors have declared that no competing pursuits exist.

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https://www.eurekalert.org/news-releases/961751

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