Heart rate variability (HRV) is the difference between the successive changes in the heartbeat cycle, and it is produced in the autonomic nervous system modulation of the sinus node of the heart. The HRV is a valuable indicator in predicting the sudden cardiac death and arrhythmic events. Traditional analysis of HRV is based on a multi-electrocardiogram (ECG), but the ECG signal acquisition is complex, so we have designed an HRV analysis system based on photoplethysmography (PPG). PPG signal is collected by a microcontroller from human’s finger, and it is sent to the terminal via USB-Serial module. The terminal software not only collects the data and plot waveforms, but also stores the data for future HRV analysis. The system is small in size, low in power consumption, and easy for operation. It is suitable for daily care no matter whether it is used at home or in a hospital.
Ambulatory electrocardiogram (ECG) monitoring can effectively reduce the risk and death rate of patients with cardiovascular diseases (CVDs). The Body Sensor Network (BSN) based ECG monitoring is a new and efficient method to protect the CVDs patients. To meet the challenges of miniaturization, low power and high signal quality of the node, we proposed a novel 50 mm×50 mm×10 mm, 30 g wireless ECG node, which includes the single-chip analog front-end AD8232, ultra-low power microprocessor MSP430F1611 and Bluetooth module HM-11. The ECG signal quality is guaranteed by the on-line digital filtering. The difference threshold algorithm results in accuracy of R-wave detection and heart rate. Experiments were carried out to test the node and the results showed that the proposed node reached the design target, and it has great potential in application of wireless ECG monitoring.
Magnetoelastic (ME) sensors, characterized by wireless, passive, low cost and high sensitivity, have widespread applications in various fields. However, its defects of large volume, high power consumption, poor portability and inconveniency for use limit the application prospects of the ME sensors. To solve this problem, the present paper shows a portable, low-power, resonance-type ME sensor detecting system based on STM32. The experimental results indicated that this detecting system allowed the ME sensor to complete the measurement of resonant frequency in different medium and different concentration, with a frequency resolution of less than 1 Hz, and the resonant frequency ratio of ME sensors in different sizes 0.933 8, closing the theoretical value of 0.942 3. Moreover, compared with the traditional impedance analyzer combined detecting system and the existing integrated detecting system, the present system has a power consumption of 0.68 W in operation and of only 2.20 mW in the dormancy mode. Therefore, the system can not only replace the original impedance analyzer combined detecting system, but also significantly improve the power control of the existing integrated detecting system, exhibiting the advantages of higher integration, portable measurement, and fine suitability for long-term monitoring.
Quantitative assessment of the symptoms of Parkinson's disease is the key for precise diagnosis and treatment and essential for long term management over years. The challenges of quantitative assessment on Parkinson's disease are rich information, ultra-low load, long term and large range monitoring in free-moving condition. In this paper, we developed wearable devices with multiple sensors to monitor and quantify the movement symptoms of Parkinson's disease. Five wearable sensors were used to record motion signals from bilateral forearms, legs and waist. A local area network based on low power Wi-Fi technology was built for long distance wireless data transmission. A software was developed for signal recording and analyzing. The size of each sensor was 39 mm×33 mm×16 mm and the weight was 18g. The sensors were rechargeable and able to run 12 hours. The wireless transmission radius is about 45 m. The wearable devices were tested in patients and normal subjects. The devices were reliable and accurate for movement monitoring in hospital.