Pulse Signal System: Sensing, Data Acquisition and Body Area Network
Shan, Kai 1985-
Heart rate variability (HRV) is an important physiological signal of the human body, which can serve as a useful biomarker for the cardiovascular health status of an individual. There are many methods to measure the HRV using electrical devices, such as ECG and PPG etc. This work presents a novel HRV detection method which is based on pressure detection on the human wrist. This method has been compared with existing HRV detection methods. In this work, the proposed system for HRV detection is based on polyvinylidene difluoride (PVDF) sensor, which can measure tiny pressure on its surface. Three PVDF sensors are mounted on the wrist, and a three-channel conditioning circuit is used to amplify signals generated by the sensors. An analog-to-digital converter and Arduino microcontroller are used to sample and process the signal. Based on the obtained signals, the HRV can be processed and detected by the proposed PVDF-sensor-based system. Another contribution of this work is in designing a wireless body area network (WBAN) to transmit data acquired on the human body. This WBAN combines two different wireless network protocols, for both efficient power consumption and data rate. Bluetooth Low Energy protocol is used for transmitting data from the microcontroller to a personal device, and Wi-Fi is used to send data to other terminals. This provides the potential for remote HRV signal monitoring. A dataset consisting of two subjects was used to experimentally validate the proposed system design and signal processing method. ECG signals are acquired from subjects with wrist pulse signals for comparison as standard signal. The waveforms of ECG signals and wrist pulse signals are compared and HRV values are calculated from these two signals separately. The result shows that HRV calculated by wrist pulse has low error rate. A test of movement effect shows the sensor can resist mild motions of wrist. Some future improvements of system design and further signal processing methods are also discussed in the last chapter.
DegreeMaster of Science (M.Sc.)
DepartmentElectrical and Computer Engineering
CommitteeDinh, Anh; Chen, Li; Zhang, Chris
Copyright DateAugust 2017
Body area network