Xianxiang Chen

Design and testing of a micromechanical resonant electrostatic field sensor

The design, fabrication and characterization testing of a high-performance micromechanical resonant electrostatic field sensor at low driving voltages is presented. Structures including sensing electrodes, shielding electrodes and suspension beam design are discussed. The electromechanical behavior around the resonant frequency is described by an equivalent electric circuit to predict the output characterization of the sensors. The device is fabricated by a surface micromachining process. With low driving voltages compared with other reported devices, the electrostatic field sensors (EFS) have a resolution of 200 V m−1, the best reported figure for a MEMS-based device used in ambient air at room temperature. A nonlinearity of 1.8% (end-point-straight-line) in a measurement range of 0–10 kV m−1 is achieved. We have also achieved an uncertainty of 4.62% for the measurement data.

A Novel High Performance Micromechanical Resonant Electrostatic Field Sensor Used In Atmospheric Electric Field Detection

This paper reports a high performance micromechanical resonant electrostatic field sensor (EFS) that is fabricated with a three-layer polysilicon surface micromachining process. The EFS has a high resolution of 100V/m when used in ambient air at room temperature. The driving voltage is 25V DC and 0.3Vp-p AC lower than other reported electrostatic comb driven devices. Experimental results show that the EFS can be succeeded in atmospheric electric field detecting.

A Bluetooth low energy approach for monitoring electrocardiography and respiration

This paper presents a design and a contrast test of an ultra-low power wireless health monitoring system capable of measuring a subjects ECG (Electrocardiography), respiration, and body temperature. The system is based on the BLE (Bluetooth Low Energy) technology which is most valued for its ultra-low power consumption. Compared to our former design using MSP430 MCU and Bluetooth 2.1, this new design is much more highly integrated and can reduce power consumption significantly, which is generally a vital problem should be considered in WSN (Wireless Sensor Network) issues. The new system can save as much as nearly 75% power consumption than the former design when working in the same mode with the sampling rate at 250 Hz, which means that the battery life can extend to 107 hours compared to 26 hours of the former one, both using a 3.7 V lithium polymer battery with the capacity of 1100 mAh. Meanwhile, the new design can connect 3 nodes simultaneously with a single PC or smart phone wirelessly; this number may increase with the new BLE stack will be released in the future.

The 3AHcare node: Health monitoring continuously

We developed and tested the Institute of Electronics, Chinese Academy of Sciences (IECAS) 3AHcare node, a health monitoring device capable of measuring a subjects ECG, blood pressure, blood oxygenation, respiration, temperature and motion - almost equivalent to the feature set of a hospital bedside patient monitor. The main contribution of this paper include: the device has been a highly integrated design incorporating the radio and all associated circuitry on a single PCB; a new noninvasive and cuff-less measurement of blood pressure using pulse wave transit time has been designed and validated. The device stores data locally on microSD flash and /or transmits via Bluetooth and/or Zigbee. We have developed a bandage vest which embeds reusable electrodes for data acquisition as well as a desktop and mobile application for real-time data telemetry. We have evaluated the performance of the device in capturing and recording ambulatory data and found the device easy to use and with high precision.

Cuff-less blood pressure measurement using pulse arrival time and a Kalman filter

The present study designs an algorithm to increase the accuracy of continuous blood pressure (BP) estimation. Pulse arrival time (PAT) has been widely used for continuous BP estimation. However, because of motion artifact and physiological activities, PAT-based methods are often troubled with low BP estimation accuracy. This paper used a signal quality modified Kalman filter to track blood pressure changes. A Kalman filter guarantees that BP estimation value is optimal in the sense of minimizing the mean square error. We propose a joint signal quality indice to adjust the measurement noise covariance, pushing the Kalman filter to weigh more heavily on measurements from cleaner data. Twenty 2 h physiological data segments selected from the MIMIC II database were used to evaluate the performance. Compared with straightforward use of the PAT-based linear regression model, the proposed model achieved higher measurement accuracy. Due to low computation complexity, the proposed algorithm can be easily transplanted into wearable sensor devices.

EMD-Based Electrocardiogram Delineation for a Wearable Low-Power ECG Monitoring Device

A novel algorithm for subtracting the artifacts from an electrocardiogram (ECG) signal and detecting the QRS complex based on empirical mode decomposition has been designed, tested, and evaluated. This method can remove both the noise of the power line interference and baseline wander from the ECG signal with minimum distortion, and R peaks can be exactly detected. The method is tested and evaluated using the records from the MIT-BIH arrhythmia database and the ECG signal data acquired from a wearable low-power ECG monitoring device. In the experiments, the correlation coefficient between the clean signal and denoised signal can be up to 0.997, and it is greater than 0.970 even in severely contaminated situations. The detection algorithm for the wearable monitoring device shows that the QRS detection rate is over 99.8% and that the sensitivity is over 99.9%. When compared with other detection methods, this proposed algorithm holds the best performance in severely noise-contaminated situations. The experiments results demonstrate that the algorithm can effectively delineate the ECG signal under different sampling rates as expected.

A design of a Band-Aid like health monitoring node for body sensor network

With the improvement of the living standards, people are paying more and more attention to their health. The development of the Body Sensor Network (BSN) technology makes it possible to continuously monitor physiological parameters such as electrocardiograph (ECG), respiratory rate, electroencephalograph arterial oxygen saturation (SpO2), body temperature, etc for a long time. In this paper, we proposed a design of a compact, ultra low-power, high-integrated physiological parameters monitoring node. The standout advantages of the proposed BSN nodes are as follows: 1) very compact: the size of the health monitoring node circuit board is only 82mm × 32mm; 2) ultra low-power consumption: the node can work for more than 120hours continuously when powered by a battery with 1900mAh; 3) highly integrated: the ECG, respiration and body temperature monitoring functions are all integrated into the small circuit board; 4) flexible communication interface: the sensed data could be transmitted through Zig bee to the gateway and then to the remote medical center for pathological analyzing or to the data base for storage through the internet, and the sensor node could also directly connect to a smart mobile phone or other end-device by the integrated BIuetooth.

Detecting work-related stress with a wearable device

Abstract Excessive stress will lower work efficiency, lead to negative emotions and even various illnesses. This paper aims at detecting work-related stress based on physiological signals measured by a wearable device. Different from common binary stress detection, this study detects three levels of stress, i.e., no stress, moderate stress and high perceived stress. The Montreal Imaging Stress Task (MIST) is used to simulate the different stress conditions, including both mental stress and psychosocial stress factors that are relevant at the workplace. A sensor-based wearable device is used to acquire the electrocardiogram (ECG) and respiration (RSP) signals from 39 participants. We extract stress-related features from ECG and RSP, and the Random Forest is used to select the optimal feature combination, which is later fed to the classifier. Four classifiers are investigated about their ability to predict the three stress levels. Finally, the combination of Random Forest and Support Vector Machine (SVM) achieve the best performance. With this method, the accuracy is improved from 78% to 84% in three states classification. And in binary stress detection, the accuracy is 94%.

Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

A systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy.A multi-parameter fusion model by back-propagation neural network is proposed to estimate the blood pressure.High accuracy BP were abtained that the meanS.D.against reference were -0.412.02 mmHg (systolic BP) and 0.462.21 mmHgdiastolic BP (DBP), respectively. The cuff-less continuous blood pressure monitoring provides reliable and invaluable information about the individuals health condition. Conventional sphygmomanometer with a cuff measures only the value of the blood pressure intermittently and the measurement process is sometimes inconvenient. In this work, a systematic approach with multi-parameter fusion has been proposed to estimate the non-invasive beat-to-beat systolic and diastolic blood pressure with high accuracy. The methods involve real-time monitoring of the electrocardiogram (ECG) and photoplethysmogram (PPG), and extracting the R peak from the ECG and relevant feature parameters from the synchronous PPG. Also, it covers the creation of the topological model of back-propagation neural network that has fifteen neurons in the input layer, ten neurons in the single interlayer, and two neurons in the output layer, where all the neurons are fully connected. As for the results, the proposed method was validated on the volunteers. The reference blood pressure (BP) is from Finometer (MIDI, Finapres Medical System, Netherlands). The results showed that the meanS.D. for the estimated systolic BP (SBP) and diastolic BP (DBP) with the proposed method against reference were 0.412.02mmHg and 0.462.21mmHg, respectively. Thus, the continuous blood pressure algorithm based on Back-Propagation neural network provides a continuous BP with a high accuracy.

Noninvasive Ambulatory Monitoring of the Electric and Mechanical Function of Heart with a Multifunction Wearable Sensor

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.