scholarly journals Ambulatory Gait Analysis : clinical application and fall risk detection

2013 ◽  
Author(s):  
R.H.J. Senden
Keyword(s):  
Gait Analysis ◽  
Clinical Application ◽  
Fall Risk ◽  
Risk Detection

Abstract P501: Mobile Daily Physical Activity and Fall Risk Monitor for Lifestyle Management, Disease Prevention and Risk Alert Generation

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Qingxue Zhang
Keyword(s):  
Physical Activity ◽  
Real World ◽  
Fall Risk ◽  
Short Term Memory ◽  
Healthy Lifestyle ◽  
Daily Physical Activity ◽  
Risk Of Death ◽  
Risk Detection ◽  
Health Innovations ◽  
Standing Up

Mobile health monitoring is driving many smart health innovations. In this research, we focus on the daily physical activity and fall risk monitoring, for intelligent activity tracking and fall risk detection purpose. Physical activity is of a tremendous value to cardiac and brain health, and has been widely agreed as a metric to improve human health, foster healthy lifestyle, and mine the impacts on diverse health issues. The risk of death can actually be lowered by 20 to 30% with moderate activities for one and half an hour per week (WHO). Besides, fall risk detection is also of huge importance since each year, there are 29% of older adults having around 29 million falls and causing 7 million fall injuries (CDC). The challenge we target is how to accurately recognize diverse physical activities and fall patterns from the biomechanical dynamics sensed by the phone. More specifically, the phone sensor is leveraged to provide real-time always-on streaming of biomechanical dynamics. Then, the data is learned by a deep learning framework (long short-term memory) for activity type and fall pattern recognition. To consider real-world scenarios, the users can put the phone in either left or write waist pockets. The users have performed 9 different kinds of activities such as walking, standing up, going upstairs and jumping, as well as 8 different kinds of fall patterns such as falling backward, sitting and falling, and falling forward with protection. A total of 11,770 activities performed by 30 subjects aging from 18 to 60 have been used to evaluate the framework, yielding an accuracy as high as 95.4%. This encouraging result shows the potential of the mobile monitor in real-world application scenarios.

Video-based Fall Risk Detection System for the Elderly

2019 ◽  
Author(s):  
Hiroaki Kingetsu ◽  
Takeshi Konno ◽  
Shuji Awai ◽  
Daisuke Fukuda ◽  
Toshihiro Sonoda
Keyword(s):  
Fall Risk ◽  
Detection System ◽  
The Elderly ◽  
Risk Detection

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 Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study

2016 ◽  
Vol 23 (6) ◽  
pp. 393-399 ◽  
Author(s):  
Ruth E. Taylor-Piliae ◽  
M. Jane Mohler ◽  
Bijan Najafi ◽  
Bruce M. Coull
Keyword(s):  
Feasibility Study ◽  
Fall Risk ◽  
Sensor Technology ◽  
Stroke Survivors ◽  
Wearable Sensor ◽  
Risk Detection

scholarly journals Automatic Fall Risk Detection based on Imbalanced Data

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yen-Hung Liu ◽  
Patrick C. K. Hung ◽  
Farkhund Iqbal ◽  
Benjamin C. M. Fung
Keyword(s):  
Fall Risk ◽  
Imbalanced Data ◽  
Risk Detection

scholarly journals Inertial sensor based gait analysis: a clinical application in patients with osteoarthritis

2012 ◽  
Vol 20 ◽  
pp. S107 ◽  
Author(s):  
L.R. brunton ◽  
S.A. Bolink ◽  
B. Grimm ◽  
S. Van Laarhoven ◽  
M. Lipperts ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Clinical Application ◽  
Inertial Sensor
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