AIMC Topic: Accidental Falls

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Adaptive Neural Sliding-Mode Controller for Alternative Control Strategies in Lower Limb Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 9, 2019
Research on control strategies for rehabilitation robots has gradually shifted from providing therapies with fixed, relatively stiff assistance to compelling alternatives with assistance or challenge strategies to maximize subject participation. Thes...
Neuronal Plasticity Lower Extremity Robotics Humans Equipment Design Algorithms Young Adult Locomotion Accidental Falls Upper Extremity Torque Healthy Volunteers Adaptation, Physiological Physical Fitness Rehabilitation
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Using supervised learning machine algorithm to identify future fallers based on gait patterns: A two-year longitudinal study.

Experimental gerontology Sep 11, 2019
INTRODUCTION: Given their major health consequences in the elderly, identifying people at risk of fall is a major challenge faced by clinicians. A lot of studies have confirmed the relationships between gait parameters and falls incidence. However, a...
Humans Algorithms Female Longitudinal Studies Postural Balance Walking Independent Living Gait Supervised Machine Learning Aged Aged, 80 and over Male Risk Assessment Pilot Projects Accidental Falls
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Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM.

Sensors (Basel, Switzerland) Aug 28, 2019
Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection w...
Humans Algorithms Vibration Accidental Falls Support Vector Machine Walking
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Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

BMC medical informatics and decision making Jul 22, 2019
BACKGROUND: Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, em...
Natural Language Processing Emergency Service, Hospital Aged, 80 and over Male Accidental Falls Humans Algorithms Retrospective Studies Female Aged Electronic Health Records Geriatrics
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A New Approach to Fall Detection Based on Improved Dual Parallel Channels Convolutional Neural Network.

Sensors (Basel, Switzerland) Jun 24, 2019
Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study ...
Electromyography Neural Networks, Computer Pattern Recognition, Automated Support Vector Machine Humans Accidental Falls Discriminant Analysis
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Fast and automatic assessment of fall risk by coupling machine learning algorithms with a depth camera to monitor simple balance tasks.

Journal of neuroengineering and rehabilitation Jun 11, 2019
BACKGROUND: Falls in the elderly constitute a major health issue associated to population ageing. Current clinical tests evaluating fall risk mostly consist in assessing balance abilities. The devices used for these tests can be expensive or inconven...
Wearable Electronic Devices Postural Balance Exercise Therapy Adult Male Accidental Falls Humans Aged Aged, 80 and over Machine Learning Female
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Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks.

Sensors (Basel, Switzerland) Apr 6, 2019
Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detectio...
Neural Networks, Computer Accidental Falls Machine Learning Humans Biosensing Techniques Algorithms Deep Learning
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Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.

Journal of medical systems Apr 4, 2019
Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strat...
Young Adult Adult Male ROC Curve Principal Component Analysis Accidental Falls Wearable Electronic Devices Neural Networks, Computer Algorithms Female Gait Humans Locomotion
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Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning.

IEEE journal of biomedical and health informatics Mar 27, 2019
Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has resulted in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, w...
Adolescent ROC Curve Humans Smartphone Neural Networks, Computer Male Female Middle Aged Signal Processing, Computer-Assisted Young Adult Aged Accidental Falls Accelerometry Adult Aged, 80 and over
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Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.

Journal of biomedical informatics Jan 9, 2019
BACKGROUND: Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several m...
Accidental Falls Natural Language Processing Data Mining Humans Machine Learning Electronic Health Records
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