DocumentCode :
151594
Title :
A low-power, wireless, wrist-worn device for long time heart rate monitoring and fall detection
Author :
Cong-Cong Zhou ; Chun-Long Tu ; Yun Gao ; Fei-Xiang Wang ; Hong-Wei Gong ; Ping Lian ; Chen He ; Xue-Song Ye
fYear :
2014
fDate :
20-23 Sept. 2014
Firstpage :
33
Lastpage :
36
Abstract :
A new low-power wrist-worn miniature device used for real-time wireless heart rate (HR) monitoring and fall detection is presented here. This device consists of sensors, signal condition circuits, microcontroller, and system communication module. Power management and algorithms are applied to achieve low power function. Using PASW Statistics 18.0(SPSS Statistics) software to analyze the 54 HR date gotten from Six subjects, we find that the average and standard deviation of the proposed device are 60.83 and 9.705 while they are 61.96 and 9.317 by using POLAR RS100(Polar Electro). The Pearson correlation coefficient is 0.975(p<;0.01). Results show that proposed device has good consistency as compared to the POLAR RS100. A low-power, low-cost MEMS accelerometer is used to detect the fall. Results show that we can detect the occurrence of a fall according to the threshold which is significant different from stationary, walking and standing up from sitting situations. When people worn the device fall down, an interrupt will be generated and sent to the microcontroller for further process immediately. 245 samples are tested, and the fall forwards detection accuracy is 93.75%. The device is useful to detect heartbeat problems in long-term vital sign monitoring such as combat medics, mountain climbers, etc. And also it is useful to detect health condition of elderly people.
Keywords :
acceleration measurement; accelerometers; bioMEMS; biomedical electronics; biomedical equipment; body sensor networks; gait analysis; geriatrics; medical signal processing; microcontrollers; microsensors; patient monitoring; photoplethysmography; statistical analysis; PASW statistics 18.0 software; POLAR RS100 Polar Electro; Pearson correlation coefficient; SPSS statistics software; combat medics; elderly people; fall detection; health condition detection; heartbeat problems; long-term vital sign monitoring; long-time heart rate monitoring; low power function; low-power low-cost MEMS accelerometer; low-power wireless wrist-worn device; low-power wrist-worn miniature device; microcontroller; mountain climbers; photoplethysmography; power management; real-time wireless heart rate monitoring; sensors; signal condition circuits; sitting situations; standard deviation; standing; system communication module; walking; Acceleration; Gain control; Heart rate; Microcontrollers; Monitoring; Wireless communication; Wireless sensor networks; fall detection; heart rate monitoring; wireless; wrist-worn longtime;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Orange Technologies (ICOT), 2014 IEEE International Conference on
Conference_Location :
Xian
Type :
conf
DOI :
10.1109/ICOT.2014.6954670
Filename :
6954670
Link To Document :
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