AIMC Topic: Accidental Falls

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Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of...
Humans Machine Learning Algorithms Databases, Factual Aged Sensitivity and Specificity Biomechanical Phenomena Accidental Falls Posture Lumbar Vertebrae Activities of Daily Living Monitoring, Ambulatory Independent Living
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Fall risk probability estimation based on supervised feature learning using public fall datasets.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection sy...
Humans Algorithms Risk Assessment Accidental Falls Models, Statistical Supervised Machine Learning Probability
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Context-aware fall detection using inertial sensors and time-of-flight transceivers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Automatic detection of falls is important for enabling people who are older to safely live independently longer within their homes. Current automated fall detection systems are typically designed using inertial sensors positioned on the body that gen...
Humans Algorithms Sensitivity and Specificity Accidental Falls Monitoring, Ambulatory
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Robotic psychophysics system for assessment, diagnosis and rehabilitation of the neurological causes of falls in the elderly.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2015
Falls are the leading causes of unintentional injuries in the elderly and thus a pose a major hazard to our ageing society. We present the FOHEPO (FOot HEight POsitioning) system to measure, diagnose and eventually rehabilitate ageing-related neurolo...
Humans Female Aged Young Adult Adult Male Middle Aged Accidental Falls Robotics Neurodegenerative Diseases Foot Leg Movement Visual Perception Feedback, Sensory Psychophysics
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Classification of older adults with/without a fall history using machine learning methods.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jan 1, 2015
Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for tr...
Humans Machine Learning Algorithms Aged Aged, 80 and over Accidental Falls Imaging, Three-Dimensional Gait
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