A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound-Proof of Concept on Human Mimicking Doll Falls

• Published in: IEEE transactions on biomedical engineering Vol. 56; no. 12; pp. 2858 - 2867
• Main Authors: Zigel, Yaniv, Litvak, Dima, Gannot, Israel
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Accidental Falls - prevention & control; Acoustic signal processing; Aged; Biomedical engineering; Biomedical signal processing; Detectors; Electric shock; Event detection; fall detector; Feasibility Studies; feature extraction; Female; Humans; Male; Monitoring, Ambulatory - methods; Older people; Pattern recognition; Pattern Recognition, Automated - methods; Reproducibility of Results; Senior citizens; Sensitivity and Specificity; Signal processing algorithms; transducers; Vibration
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2009.2030171

Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.