@incollection{eprints1249,
           month = {January},
       publisher = {IEEE},
          author = {Stefano Abbate and Marco Avvenuti and Guglielmo Cola and Paolo Corsini and Janet Light and Alessio Vecchio},
           title = {Recognition of false alarms in fall detection systems},
       booktitle = {Consumer Communications and Networking Conference (CCNC)},
           pages = {23 --28},
            year = {2011},
             url = {http://eprints.imtlucca.it/1249/},
        keywords = {accelerometer; elderly population; fall detection systems; fall recognition; false alarms; health services costs; hospitalization; injury-related deaths; specific movement patterns; wearable device;accelerometers;biomechanics;biomedical measurement;geriatrics;health care;injuries;medical signal detection;patient monitoring;telemedicine;},
        abstract = {Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced.}
}