AIMC Topic: Accidental Falls

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An Advanced Self-Similarity Measure: Average of Level-Pairwise Hurst Exponent Estimates (ALPHEE).

IEEE transactions on bio-medical engineering
Many natural processes are characterized by complex patterns of self-similarity, where repetitive structures occur across different resolutions. The Hurst exponent is a key parameter used to quantify this self-similarity. While wavelet-based techniqu...

Optimization enabled ensemble based deep learning model for elderly falling risk prediction.

Computer methods in biomechanics and biomedical engineering
Predicting fall risk in the elderly is crucial for enhancing safety and well-being. Aging and chronic diseases often impair balance, increasing fall risk. This study aims to develop an advanced fall risk prediction model using an optimized deep learn...

Aging With Artificial Intelligence: How Technology Enhances Older Adults' Health and Independence.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: As the global population ages healthcare challenges are escalating. Frailty, a clinical syndrome characterized by decreased reserve and resilience to stressors, is critically linked to adverse health outcomes in older adults. However, art...

Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine learning: a proof-of-concept study.

BMJ military health
INTRODUCTION: Injuries induced by falls represent the main cause of failure in the French Navy Special Forces selection course. In the present study, we made the assumption that probing the posture might contribute to predicting the risk of fall-rela...

Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Scientific reports
Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. This study included 13,074 middle-aged and older adults from the China health and retirement longitudin...

Machine Learning Scoring Reveals Increased Frequency of Falls Proximal to Death in Drosophila melanogaster.

The journals of gerontology. Series A, Biological sciences and medical sciences
Falls are a significant cause of human disability and death. Risk factors include normal aging, neurodegenerative disease, and sarcopenia. Drosophila melanogaster is a powerful model for study of normal aging and for modeling human neurodegenerative ...

Development of an Assistance Robot for Fall Detection and Reporting in Healthcare.

Studies in health technology and informatics
Falls pose a substantial risk to elderly individuals, especially those over 65, often leading to severe consequences. This project investigates the potential of the tēmi robot for fall detection in care facilities and its integration into a simulated...

Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.

European journal of neurology
BACKGROUND: Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurat...

Automated Fall Detection in Smart Homes Using Multiple Radars and Machine Learning Classifiers.

Studies in health technology and informatics
Falls pose a significant risk, especially among elderly persons. Recently, radar sensors have been explored for fall detection. In this study, an attempt has been made to classify fall detection using multiple radars, machine learning (ML) classifier...

Automated identification of fall-related injuries in unstructured clinical notes.

American journal of epidemiology
Fall-related injuries (FRIs) are a major cause of hospitalizations among older patients, but identifying them in unstructured clinical notes poses challenges for large-scale research. In this study, we developed and evaluated natural language process...