A multifunction wearable noninvasive ambulatory monitoring sensor based on Electrocardiogram (ECG) and Impedance Cardiography (ICG) has been designed, fabricated and tested. The electric function (with ECG) and mechanical function (with ICG) of the heart can be monitored simultaneously and continuously. Based the RR serials computed from ECG waveform, the sympathetic and parasympathetic function of the automatic nervous system can also be monitored at the same time. The physical activity is monitored with a 3-axis accelerometer and a 3-axis gyroscope chip on the sensor board. The physical activity data is very important because heart rate and cardiac output etc has different standard value under different movement intensity conditions. Hemodynamic parameters such as stroke volume (SV), cardiac output (CO) and cardiac index (CI) can be estimated according to Kubicek Formula by extracting characteristic points and characteristic periods of the ICG Signal. CI is CO divided by body surface area. CI is comparable among people because it is not influenced by the body height and body weight. The detection accuracy has also been validated with a commercial patient monitor from Mind Ray company, the correlation coefficient is 0.83.

This paper presents a novel wireless health monitoring system based on an embedded reconfigurable platform with CPU and FPGA integrated on a single board. Physiological signals and parameters are acquired by BLE (Bluetooth Low Energy) sensor nodes and transferred to the platform via a BLE dongle connected through RS-232 interface. A UWT (Undecimated Wavelet Transform) core is implemented in the built-in FPGA chip to accelerate signal analysis. The test result shows that this FPGA core runs much faster than the CPU-based UWT function implemented in LabVIEW Real-Time module.

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This paper presents a wearable physiological parameters monitoring device real time monitoring electrocardiograph (ECG), respiration, blood oxygen saturation, blood pressure, motion state and temperature continuously, online analysis and displaying the result in personal computer. Especially, Improved Pan-Tompkins Algorithm was embedded into wearable physiological parameters monitoring device to detect R peak, and further compute heart rate. Based upon the analysis of QRS frequency, the slope and the threshold decision, the improved algorithm can reliably recognize R peak of QRS complexes. Compared to the traditional Pan-Tompkins, there are three improvements. First one is accurately calculating heart rate even in a slow-moving state; another is wider sampling rate-500 and 1000 sampling rate or any other sampling rate; the last one is effectively avoiding the finite word-length effect during calculation in float type. The improved Pan-Tompkins algorithm makes ECG measurement more accurate and more flexible. Based on 20 volunteers' experimental tests, the fully-integration system with improved Pan-Tompkins Algorithm can accurately monitor the real-time R peak and other physiological indicators in a calm status, even in slow-moving status the system works well.

As the nerve cell of the Internet of Things, WSN (wireless sensor network) have been the most important part of our life. Even more WBSN (wireless body sensor network) as one kind of WSN have been one part of our body. Sensor technology, Computer technology, wireless technology, semiconductor technology and so on all these development has made sensor network node so smaller, lighter and more multifunctional that can be dressed in portably. The sensor nodes can sample many of vital signals such as ECG, EEG, EKG, SPO2, body-temperature, Glucose, Heart Rate and so on, activities of daily living and gait information, environment information. All of information collected is transferred to extracorporeal data center. Based on the information collected something related to health can be analyzed.