A low-power, wireless, wrist-worn device for long time heart rate monitoring and fall detection

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.

[1]  SeongHwan Cho,et al.  A bio-impedance measurement system for portable monitoring of heart rate and pulse wave velocity using small body area , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[2]  C. M. Lee,et al.  Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach , 2003, IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 2003..

[3]  Chan-kyu park,et al.  Artifact-resistant design of a wrist-type heart rate monitoring device , 2009, 2009 11th International Conference on Advanced Communication Technology.

[4]  Dipali Bansal,et al.  Real time acquisition and PC to PC wireless transmission of human carotid pulse waveform , 2009, Comput. Biol. Medicine.

[5]  Frederick A. Masoudi,et al.  The prognostic importance of abnormal heart rate recovery and chronotropic response among exercise treadmill test patients. , 2008, American heart journal.

[6]  Jean-Claude Tardif,et al.  Resting heart rate in cardiovascular disease. , 2007, Journal of the American College of Cardiology.