Title :
Wearable Sensors for Reliable Fall Detection
Author :
Chen, Jay ; Kwong, Karric ; Chang, Dennis ; Luk, Jerry ; Bajcsy, Ruzena
Author_Institution :
Dept. of Electr. Eng., California Univ., Berkeley, CA
Abstract :
Unintentional falls are a common cause of severe injury in the elderly population. By introducing small, non-invasive sensor motes in conjunction with a wireless network, the Ivy Project aims to provide a path towards more independent living for the elderly. Using a small device worn on the waist and a network of fixed motes in the home environment, we can detect the occurrence of a fall and the location of the victim. Low-cost and low-power MEMS accelerometers are used to detect the fall while RF signal strength is used to locate the person
Keywords :
accelerometers; ad hoc networks; biomechanics; biomedical equipment; geriatrics; microsensors; wireless sensor networks; elderly; low-cost low-power MEMS accelerometers; noninvasive sensor motes; reliable fall detection; wearable sensors; wireless network; Accelerometers; Detection algorithms; Detectors; Injuries; Medical services; Micromechanical devices; Senior citizens; Space technology; Wearable sensors; Wireless sensor networks;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1617246
