Bluesky Facebook Reddit Email

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

08.18.22 | PLOS

Apple iPhone 17 Pro

Apple iPhone 17 Pro delivers top performance and advanced cameras for field documentation, data collection, and secure research communications.
Machine learning model predicts fall risk for lower limb amputees with up to 80% accuracy, with implications for future smartphone apps
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. 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.

PLOS Digital Health

10.1371/journal.pdig.0000088

Observational study

People

Automated step detection with 6-minute walk test smartphone sensors signals for fall risk classificiation in lower limb amputees

Competing interests: The authors have declared that no competing interests exist.

Keywords

Amputation Applied Sciences and Engineering Health and Medicine Human Health Machine Learning Public Health Smartphones

Article Information

Journal
PLOS Digital Health
DOI
10.1371/journal.pdig.0000088
Method of Research
Observational study
Subject of Research
People
Article Title
Automated step detection with 6-minute walk test smartphone sensors signals for fall risk classificiation in lower limb amputees
COI Statement
Competing interests: The authors have declared that no competing interests exist.

Contact Information

Claire Turner
PLOS
digitalhealth@plos.org

How to Cite This Article

APA:
PLOS. (2022, August 18). Machine learning model predicts fall risk for lower limb amputees with up to 80% accuracy, with implications for future smartphone apps. Brightsurf News. https://www.brightsurf.com/news/12DJEOY1/machine-learning-model-predicts-fall-risk-for-lower-limb-amputees-with-up-to-80-accuracy-with-implications-for-future-smartphone-apps.html
MLA:
"Machine learning model predicts fall risk for lower limb amputees with up to 80% accuracy, with implications for future smartphone apps." Brightsurf News, Aug. 18 2022, https://www.brightsurf.com/news/12DJEOY1/machine-learning-model-predicts-fall-risk-for-lower-limb-amputees-with-up-to-80-accuracy-with-implications-for-future-smartphone-apps.html.