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Accidental Falls

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Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.

BMC public health
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...

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
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...

Transfer Learning on Small Datasets for Improved Fall Detection.

Sensors (Basel, Switzerland)
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...

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data.

Sensors (Basel, Switzerland)
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...

A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection.

Sensors (Basel, Switzerland)
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...

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
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...

Predicting future falls in older people using natural language processing of general practitioners' clinical notes.

Age and ageing
BACKGROUND: Falls in older people are common and morbid. Prediction models can help identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity to develop automated prediction tools that may help to identify fall...

Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data...

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

Computers, informatics, nursing : CIN
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...

Walking and falling: Using robot simulations to model the role of errors in infant walking.

Developmental science
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur o...