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Development of an automated fall detection device specific to wheelchair users
L. A. Rice
, L. Abou
, A. Fliflet
, P. Presti
, J. J. Sosnoff
,
H. P. Mahajan
, M. L. Frechette
Health and Kinesiology
College of Applied Health Sciences
Beckman Institute for Advanced Science and Technology
Interdisciplinary Health Sciences Institute
Research output
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Article
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peer-review
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Keyphrases
Device-independent
100%
Detection Device
100%
Wheelchair Users
100%
Fall Detection
100%
Wheelchair
83%
Older Adults
50%
Wrist
33%
Feature Vector
33%
Accelerometer
33%
Fast Response
16%
False Alarm
16%
Best Prediction
16%
Extended Period
16%
Sampling Window
16%
Community Setting
16%
Fall-related Injuries
16%
Window-based
16%
Trial Data
16%
Manual Wheelchair
16%
Outcome Prediction
16%
Long-term Care
16%
Health Anxiety
16%
Activities of Daily Living
16%
Data Window
16%
Accurate Response
16%
Learning Classifiers
16%
Classification Features
16%
Injurious Falls
16%
K-fold Cross-validation
16%
Ethical Approval
16%
SVM Method
16%
Apple Watch
16%
Repeated Falls
16%
Fall Detection System
16%
Medicine and Dentistry
Accelerometer
100%
Cross-Validation
50%
Long-Term Care
50%
Nursing and Health Professions
Accelerometer
100%
Manual Wheelchair
50%
Long Term Care
50%