AIMC Topic: Accidental Falls

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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...
Gait Quality of Life Accidental Falls Neural Networks, Computer Humans Machine Learning
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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...
Humans Artificial Intelligence Walking Locomotion Infant Infant Behavior Accidental Falls
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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...
Humans Algorithms Computers Accidental Falls Neural Networks, Computer Artificial Intelligence
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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 Shoes Accidental Falls Humans Canada Neural Networks, Computer Walking
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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...
Activities of Daily Living Antiparkinson Agents Parkinson Disease Postural Balance Supervised Machine Learning Models, Theoretical Age Factors Male Middle Aged Female Humans Severity of Illness Index Risk Factors Risk Assessment Accidental Falls Prognosis Aged
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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...
Monitoring, Ambulatory Accelerometry Accidental Falls Support Vector Machine Machine Learning Algorithms Humans
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Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Sensors (Basel, Switzerland) Oct 28, 2020
Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measur...
Humans Deep Learning Wearable Electronic Devices Acceleration Accidental Falls
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Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.

Sensors (Basel, Switzerland) Sep 19, 2020
Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessm...
Aged Risk Assessment Deep Learning Humans Wrist Gait Analysis Accidental Falls Wearable Electronic Devices
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Automatic fall detection using region-based convolutional neural network.

International journal of injury control and safety promotion Sep 15, 2020
The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learning tasks. However, these efforts have not succeeded in determining an ideal classifier or feature ex...
Accidental Falls Humans Image Processing, Computer-Assisted Neural Networks, Computer Monitoring, Ambulatory Machine Learning
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Application of Machine Learning Methods in Nursing Home Research.

International journal of environmental research and public health Aug 27, 2020
A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random...
Nursing Research Male Algorithms Activities of Daily Living Accidental Falls Female Humans Nursing Homes Machine Learning Support Vector Machine
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