Human fall detection on embedded platform using depth maps and wireless accelerometer

Highlights • An embedded system for fully automatic fall detection. • A method to achieve reliable fall detection with low false alarm ratio. • An algorithm with low computational demands for person extraction in depth images. • A freely available database for evaluation of fall detection, consistin...

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Veröffentlicht in:	Computer methods and programs in biomedicine 2014-12, Vol.117 (3), p.489-501
Hauptverfasser:	Kwolek, Bogdan, Kepski, Michal
Format:	Artikel
Sprache:	eng
Schlagworte:	Acceleration Accelerometers Accelerometry - instrumentation Accelerometry - methods Accidental Falls - prevention & control Activities of Daily Living Adult Algorithms Applied sciences Artificial intelligence Assistive technology Biological and medical sciences Computer programs Computer science control theory systems Computer systems and distributed systems. User interface Computerized, statistical medical data processing and models in biomedicine Depth image analysis Detectors Exact sciences and technology Facility Design and Construction Fall detection False alarms Humans Inertial Internal Medicine Medical computing and teaching Medical sciences Middle Aged Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Movement Other Pattern recognition. Digital image processing. Computational geometry Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects) Reproducibility of Results Sensor technology for smart homes Software Support Vector Machine Technology. Biomaterials. Equipments. Material. Instrumentation
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Zusammenfassung:	Highlights • An embedded system for fully automatic fall detection. • A method to achieve reliable fall detection with low false alarm ratio. • An algorithm with low computational demands for person extraction in depth images. • A freely available database for evaluation of fall detection, consisting of both accelerometric and depth data.
ISSN:	0169-2607 1872-7565
DOI:	10.1016/j.cmpb.2014.09.005