AIMC Topic: Accidental Falls

Clear Filters Showing 81 to 90 of 200 articles

Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning.

PloS one Feb 25, 2021
Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body's center-of-pressure displacement (statokinesigram) while the...
Postural Balance Parkinsonian Disorders Male Accidental Falls Models, Neurological Aged Aged, 80 and over Machine Learning Humans Female
View on PubMed DOI

Comprehensive Review of Vision-Based Fall Detection Systems.

Sensors (Basel, Switzerland) Feb 1, 2021
Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the m...
Humans Accidental Falls Gait Lighting Databases, Factual Aged Neural Networks, Computer
View on PubMed DOI

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.

Sensors (Basel, Switzerland) Jan 30, 2021
Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) usi...
Male Accidental Falls Wearable Electronic Devices Female Aged Activities of Daily Living Machine Learning Algorithms Humans
View on PubMed DOI

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Scientific reports Jan 28, 2021
Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replace...
Machine Learning Accidental Falls Activities of Daily Living Humans Accelerometry
View on PubMed DOI

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition.

Sensors (Basel, Switzerland) Jan 25, 2021
With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis d...
Accidental Falls Neural Networks, Computer Machine Learning Humans Gait Quality of Life
View on PubMed DOI

The impact of errors in infant development: Falling like a baby.

Developmental science Jan 4, 2021
What is the role of errors in infants' acquisition of basic skills such as walking, skills that require immense amounts of practice to become flexible and generative? Do infants change their behaviors based on negative feedback from errors, as sugges...
Locomotion Infant Behavior Accidental Falls Walking Artificial Intelligence Infant Humans
View on PubMed DOI

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.

Sensors (Basel, Switzerland) Dec 17, 2020
With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computati...
Neural Networks, Computer Artificial Intelligence Computers Algorithms Humans Accidental Falls
View on PubMed DOI

Deep Neural Network for Slip Detection on Ice Surface.

Sensors (Basel, Switzerland) Dec 2, 2020
Slip-induced falls are among the most common causes of major occupational injuries and economic loss in Canada. Identifying the risk factors associated with slip events is key to developing preventive solutions to reduce falls. One factor is the slip...
Ice Humans Accidental Falls Canada Neural Networks, Computer Walking Shoes
View on PubMed DOI

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Parkinsonism & related disorders Nov 19, 2020
BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable h...
Parkinson Disease Postural Balance Supervised Machine Learning Models, Theoretical Age Factors Activities of Daily Living Risk Factors Male Female Humans Antiparkinson Agents Aged Middle Aged Prognosis Severity of Illness Index Risk Assessment Accidental Falls
View on PubMed DOI

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.

Sensors (Basel, Switzerland) Nov 13, 2020
Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We a...
Humans Machine Learning Monitoring, Ambulatory Algorithms Accidental Falls Support Vector Machine Accelerometry
View on PubMed DOI
Popular Topics
Recent Journals