AIMC Topic: Accidental Falls
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A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.
Sensors (Basel, Switzerland)
Mar 6, 2020
Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. T...
Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.
International journal of medical informatics
Mar 3, 2020
OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk...
Effects of exoskeletal gait assistance on the recovery motion following tripping.
PloS one
Feb 24, 2020
Physical assistant robots improve the user's ability to walk. However, they also potentially affect recovery motion following tripping. The assist algorithm should not interfere with the recovery motion, and should enhance the ability of the user to ...
Tip-Over Stability Analysis of a Pelvic Support Walking Robot.
Journal of healthcare engineering
Feb 6, 2020
Discussed in this paper is the tip-over stability analysis of a pelvic support walking robot. To improve the activities of daily living (ADL) in hemiplegic patients, a pelvic support walking robot is proposed to help patients facilitating their rehab...
System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks.
Journal of medical systems
Jan 6, 2020
The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-...
Land-walking vs. water-walking interventions in older adults: Effects on aerobic fitness.
Journal of sport and health science
Dec 23, 2019
BACKGROUND: Low cardiorespiratory fitness is an independent predictor of all-cause and cardiovascular mortality, and interventions that increase fitness reduce risk. Water-walking decreases musculoskeletal impact and risk of falls in older individual...
Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters.
IEEE journal of biomedical and health informatics
Dec 11, 2019
Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to faci...
Wearable Fall Detector Using Recurrent Neural Networks.
Sensors (Basel, Switzerland)
Nov 8, 2019
Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analy...
Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.
Scientific reports
Nov 6, 2019
Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation im...
A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.
Computers in biology and medicine
Oct 30, 2019
The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification...
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