News Release

Machine learning model predicts fall risk for lower limb amputees with up to 80% accuracy, with implications for future smartphone apps

Peer-Reviewed Publication

PLOS

Machine learning model predicts fall risk for lower limb amputees with up to 80% accuracy, with implications for future smartphone apps

image: The Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test App was used to collect data in this research. Left: A participant completes a walk test with a smartphone attached to the lower back. Right: User interface of TOHRC Walk Test App after the walk test is complete. view more 

Credit: Juneau P, et al., 2022, PLOS Digital Health, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

In your coverage, please use this URL to provide access to the freely available 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 walk test smartphone sensors signals for fall risk classification in lower limb amputees

Author Countries: Canada, Slovenia

Funding: This research 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 role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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