AIMC Topic: Accidental Falls

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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
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...

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...

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...

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...

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
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...

Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls.

Journal of healthcare engineering
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...

Inertial Data-Based AI Approaches for ADL and Fall Recognition.

Sensors (Basel, Switzerland)
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...

Split BiRNN for real-time activity recognition using radar and deep learning.

Scientific reports
Radar systems can be used to perform human activity recognition in a privacy preserving manner. This can be achieved by using Deep Neural Networks, which are able to effectively process the complex radar data. Often these networks are large and do no...

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports tra...