AIMC Topic: Accidental Falls

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Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis.

Computers, informatics, nursing : CIN Jul 1, 2023
Because of an aging population worldwide, the increasing prevalence of falls and their consequent injuries are becoming a safety, health, and social-care issue among elderly people. We conducted a meta-analysis to investigate the benchmark of predict...
Humans Artificial Intelligence Sensitivity and Specificity Accidental Falls Patients Electronic Health Records Aged
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A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection.

Sensors (Basel, Switzerland) Feb 24, 2023
Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, am...
Aged Accidental Falls Robotics Humans Interior Design and Furnishings Infrared Rays Human Activities
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Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients.

Health and quality of life outcomes Feb 21, 2023
BACKGROUND: In the field of neurorehabilitation, robot-assisted therapy (RAT) and virtual reality (VR) have so far shown promising evidence on multiple motor and functional outcomes. The related effectiveness on patients' health-related quality of li...
Quality of Life Accidental Falls Humans Robotics Fear Stroke Virtual Reality Stroke Rehabilitation
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Transfer Learning on Small Datasets for Improved Fall Detection.

Sensors (Basel, Switzerland) Jan 18, 2023
Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in r...
Accidental Falls Algorithms Aged Humans Machine Learning
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Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data.

Sensors (Basel, Switzerland) Jan 12, 2023
Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluc...
Gait Somatoform Disorders Accidental Falls Stroke Reflex, Startle Humans Artificial Intelligence Risk Assessment
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Cost-Effectiveness and Value of Information Analysis of an Ambient Intelligent Geriatric Management (AmbIGeM) System Compared to Usual Care to Prevent Falls in Older People in Hospitals.

Applied health economics and health policy Dec 9, 2022
BACKGROUND: The Ambient Intelligent Geriatric Management (AmbIGeM) system combines wearable sensors with artificial intelligence to trigger alerts to hospital staff before a fall. A clinical trial found no effect across a heterogenous population, but...
Aged Humans Australia Hospitals Accidental Falls Cost-Benefit Analysis Artificial Intelligence
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Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.

BMC public health Nov 29, 2022
BACKGROUND: Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of...
Machine Learning Risk Factors Aged Accidental Falls Humans Belgium Independent Living
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Deep Learning-Based Near-Fall Detection Algorithm for Fall Risk Monitoring System Using a Single Inertial Measurement Unit.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 1, 2022
Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation s...
Accidental Falls Activities of Daily Living Monitoring, Ambulatory Aged Humans Deep Learning Algorithms
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Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.

Journal of healthcare engineering Aug 1, 2022
BACKGROUND: An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of pa...
Risk Factors Infant Humans Accidental Falls Machine Learning Bayes Theorem Delivery of Health Care
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Inertial Data-Based AI Approaches for ADL and Fall Recognition.

Sensors (Basel, Switzerland) May 26, 2022
The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, whic...
Humans Activities of Daily Living Algorithms Neural Networks, Computer Accidental Falls Artificial Intelligence
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