The Gait Analysis About the Elder Fall Risk

2012 ◽  
Author(s):  
Zhang Meng ◽  
Qin Hongwu
Keyword(s):  
Gait Analysis ◽  
Fall Risk

scholarly journals Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

2011 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Benoit Caby ◽  
Suzanne Kieffer ◽  
Marie de Saint Hubert ◽  
Gerald Cremer ◽  
Benoit Macq
Keyword(s):  
Risk Assessment ◽  
Feature Extraction ◽  
Gait Analysis ◽  
Fall Risk ◽  
Fall Risk Assessment ◽  
Feature Extraction And Selection ◽  
Selection For

scholarly journals Dataset for gait analysis and assessment of fall risk for older adults

Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106550
Author(s):  
Pablo E. Caicedo ◽  
Carlos F. Rengifo ◽  
Luis E. Rodriguez ◽  
Wilson A. Sierra ◽  
Maria C. Gómez
Keyword(s):  
Older Adults ◽  
Gait Analysis ◽  
Fall Risk

Development of a dynamic fall risk profile in elderly nursing home residents: A free field gait analysis based study

2021 ◽  
Vol 93 ◽  
pp. 104294
Author(s):  
Eduard Witiko Unger ◽  
Tina Histing ◽  
Mika Frieda Rollmann ◽  
Marcel Orth ◽  
Esther Herath ◽  
...  
Keyword(s):  
Nursing Home ◽  
Gait Analysis ◽  
Fall Risk ◽  
Free Field ◽  
Nursing Home Residents ◽  
Risk Profile

scholarly journals Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6992
Author(s):  
Rana Zia Ur Rehman ◽  
Yuhan Zhou ◽  
Silvia Del Din ◽  
Lisa Alcock ◽  
Clint Hansen ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Fall Risk ◽  
Clinical Decision Making ◽  
Principal Component ◽  
Clinical Decision ◽  
Poor Quality ◽  
Neurological Patients ◽  
Stride Duration

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Reliability and clinical correlates of 3D-accelerometry based gait analysis outcomes according to age and fall-risk

2011 ◽  
Vol 33 (3) ◽  
pp. 366-372 ◽  
Author(s):  
Ivan Bautmans ◽  
Bart Jansen ◽  
Bart Van Keymolen ◽  
Tony Mets
Keyword(s):  
Gait Analysis ◽  
Fall Risk ◽  
Clinical Correlates
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