AIMC Topic: Accidental Falls
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An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.
Journal of medical Internet research
Aug 5, 2024
BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, w...
A machine learning approach to determine the risk factors for fall in multiple sclerosis.
BMC medical informatics and decision making
Jul 30, 2024
BACKGROUND: Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial. This study aims to investig...
Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.
Sensors (Basel, Switzerland)
Jul 29, 2024
Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better i...
Development and External Validation of a Machine Learning-based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study.
Journal of the American Medical Directors Association
Jul 24, 2024
OBJECTIVES: To develop and externally validate a machine learning-based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification...
Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network.
Sensors (Basel, Switzerland)
Jul 17, 2024
The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated...
Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.
Sensors (Basel, Switzerland)
Jul 16, 2024
Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagno...
Privacy-Preserving Cameras for Fall Detection: Data Acquisition for Artificial Intelligence.
Computers, informatics, nursing : CIN
Jul 1, 2024
Enhancing fall risk assessment: instrumenting vision with deep learning during walks.
Journal of neuroengineering and rehabilitation
Jun 22, 2024
BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking pro...
Falls Prevention Using AI and Remote Surveillance in Nursing Homes.
Journal of the American Medical Directors Association
Jun 13, 2024
The older population of United States is growing, with more adults having complicated medical conditions being admitted into nursing facilities and assisted living facilities. With the COVID-19 pandemic, the biggest challenge has been falls preventio...
Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis.
Multiple sclerosis and related disorders
Jun 10, 2024
INTRODUCTION: Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. The progressive impairment of gait is one of the most important pathognomic symptoms which are associated with falls and fear of fall...
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