Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia-Reference-Cited by-同舟云学术

Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

Published:2023-07 Issue:7 Volume:27 Page:3599-3609
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Adeli Vida¹^ORCID,Korhani Navid¹^ORCID,Sabo Andrea¹^ORCID,Mehdizadeh Sina²^ORCID,Mansfield Avril¹^ORCID,Flint Alastair¹^ORCID,Iaboni Andrea¹^ORCID,Taati Babak²^ORCID

Affiliation:

1. KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

2. KITE, Toronto Rehabilitation Institute, UHN, Toronto, ON, Canada

Funder

Walter and Maria Schroeder Institute for Brain Innovation and Recovery

Toronto Rehabilitation Institute – University Health Network

Canadian Institutes of Health Research

Natural Sciences and Engineering Research Council of Canada

Alzheimer's Association & Brain Canada

Data Sciences Institute

University of Toronto

AMS Healthcare Fellowship in Compassion and Artificial Intelligence

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10171108/10102699.pdf?arnumber=10102699

Reference60 articles.

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2. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies

3. SMOTE: Synthetic Minority Over-sampling Technique

4. Risk Factors Associated with Falls in Older Adults with Dementia: A Systematic Review

5. Evaluating Mobility in Advanced Dementia: A Scoping Review and Feasibility Analysis

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