AIMC Topic: Accidental Falls
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A lightweight approach to gait abnormality detection for At Home health monitoring.
Computers in biology and medicine
Mar 30, 2025
Gait abnormality detection is a growing application in machine learning based health assessment due to its potential in domains from clinical health reviews to at home health monitoring. This latter application is of particular use for older adults, ...
[How older people are learning through artificial intelligence-assisted health technologies : Cute seals and nervous fall sensors].
Zeitschrift fur Gerontologie und Geriatrie
Mar 27, 2025
BACKGROUND: With the growing use of artificial intelligence (AI) in various areas of life, AI technologies are increasingly being developed for the nursing and care of older people and are intended to contribute to greater safety for older people in ...
Fall recognition using a three stream spatio temporal GCN model with adaptive feature aggregation.
Scientific reports
Mar 27, 2025
The prevention of falls is paramount in modern healthcare, particularly for the elderly, as falls can lead to severe injuries or even fatalities. Additionally, the growing incidence of falls among the elderly, coupled with the urgent need to prevent ...
Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning.
Scientific reports
Mar 26, 2025
Tai Chi, an Asian martial art, is renowned for its health benefits, particularly in promoting healthy aging among older adults, improving balance, and reducing fall risk. However, methodological challenges hinder the objective measurement of adherenc...
Integrating machine learning and a large language model to construct a domain knowledge graph for reducing the risk of fall-from-height accidents.
Accident; analysis and prevention
Mar 20, 2025
Fall-from-height (FFH) accidents remain a major source of workplace injuries and fatalities. Fall protection systems (FPS) are critical for preventing falls in the work-at-height (WAH) environment. However, challenges in designing and selecting effec...
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.
BMC geriatrics
Mar 17, 2025
BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using ma...
Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique.
Sensors (Basel, Switzerland)
Feb 27, 2025
Slips, trips, and falls (STFs) are a major occupational hazard that contributes significantly to workplace injuries and the associated financial costs. The application of traditional fall detection techniques in the real world is limited because they...
Machine learning using random forest to differentiate between blow and fall situations of head trauma.
International journal of legal medicine
Feb 22, 2025
Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, particularly when it comes to distinguishing between falls or inflicted blows. The objective of this study...
Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.
PloS one
Feb 3, 2025
BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a sign...
Constructing a fall risk prediction model for hospitalized patients using machine learning.
BMC public health
Jan 20, 2025
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.
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