A wireless ExG interface for patch-type ECG holter and EMG-controlled robot hand

This paper presents a wearable electrophysiological interface with enhanced immunity to motion artifacts. Anti-artifact schemes, including a patch-type modular structure and real-time automatic level adjustment, are proposed and verified in two wireless system prototypes of a patch-type electrocardiogram (ECG) module and an electromyogram (EMG)-based robot-hand controller. Their common ExG readout integrated circuit (ROIC), which is reconfigurable for multiple physiological interfaces, is designed and fabricated in a 0.18 ??m CMOS process. Moreover, analog pre-processing structures based on envelope detection are integrated with one another to mitigate signal processing burdens in the digital domain effectivel

Wavelet-based EMG Sensing Interface for Pattern Recognition

Department of Electrical EngineeringAs interest in healthcare and smart devices has increased in recent years, the studies that are sensing and analyzing various bio signals, such as EMG, ECG, and EEG, have been growing. These studies and advances in smart devices have allowed human to increase access to their own physical information. With the physical information, human can diagnose himself or herself. These advances in technology will improve the quality of human life and provide solutions in various fields. The convergence of information and communication technologies has led to the fourth industrial revolution and the development of artificial intelligence, big data and the Internet of Things(IoT) by increasing computing power has led to various data analysis using machine learning. Various fields are moving toward the next level using machine learning, and this trend is also happening in the healthcare field. The era of self-diagnosis begins when medical knowledge, which had previously been entrusted to doctors is passed directly to consumers through big data and machine learning. Thanks to these developments, the healthcare interface, such as front-end integrated chip, is also working to leverage machine learning to deliver various solutions to consumers. Existing papers related to bio signals are focused on reducing power consumption, allowing long-term monitoring or reducing various noise. This paper provides an idea to extend the scope of data processes through machine learning while maintaining existing trends. Wavelet transform is implemented as a circuit to reduce computing power and eliminate specific frequency range including noise and motion artifact. The data from the chip is transmitted to external device (MATLAB) by wireless communication (Bluetooth) to be analyzed by machine learning. This paper present wavelet-based EMG sensing interface which includes front-end amplifier, wavelet filters, Analog to digital converter and Microcontroller. The main idea of the paper is front-end amplifiers which reduce a noise and motion artifact, wavelet filters that decompose the input signal for wavelet transform and machine learning for gesture recognition.ope

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Department of Electrical EngineeringA Sensor system is advanced along sensor technologies are developed. The performance improvement of sensor system can be expected by using the internet of things (IoT) communication technology and artificial neural network (ANN) for data processing and computation. Sensors or systems exchanged the data through this wireless connectivity, and various systems and applications are possible to implement by utilizing the advanced technologies. And the collected data is computed using by the ANN and the efficiency of system can be also improved. Gas monitoring system is widely need from the daily life to hazardous workplace. Harmful gas can cause a respiratory disease and some gas include cancer-causing component. Even though it may cause dangerous situation due to explosion. There are various kinds of hazardous gas and its characteristics that effect on human body are different each gas. The optimal design of gas monitoring system is necessary due to each gas has different criteria such as the permissible concentration and exposure time. Therefore, in this thesis, conventional sensor system configuration, operation, and limitation are described and gas monitoring system with wireless connectivity and neural network is proposed to improve the overall efficiency. As I already mentioned above, dangerous concentration and permissible exposure time are different depending on gas types. During the gas monitoring, gas concentration is lower than a permissible level in most of case. Thus, the gas monitoring is enough with low resolution for saving the power consumption in this situation. When detecting the gas, the high-resolution is required for the accurate concentration detecting. If the gas type is varied in the above situation, the amount of calculation increases exponentially. Therefore, in the conventional systems, target specifications are decided by the highest requirement in the whole situation, and it occurs increasing the cost and complexity of readout integrated circuit (ROIC) and system. In order to optimize the specification, the ANN and adaptive ROIC are utilized to compute the complex situation and huge data processing. Thus, gas monitoring system with learning-based algorithm is proposed to improve its efficiency. In order to optimize the operation depending on situation, dual-mode ROIC that monitoring mode and precision mode is implemented. If the present gas concentration is decided to safe, monitoring mode is operated with minimal detecting accuracy for saving the power consumption. The precision mode is switched when the high-resolution or hazardous situation are detected. The additional calibration circuits are necessary for the high-resolution implementation, and it has more power consumption and design complexity. A high-resolution Analog-to-digital converter (ADC) is kind of challenges to design with efficiency way. Therefore, in order to reduce the effective resolution of ADC and power consumption, zooming correlated double sampling (CDS) circuit and prediction successive approximation register (SAR) ADC are proposed for performance optimization into precision mode. A Microelectromechanical systems (MEMS) based gas sensor has high-integration and high sensitivity, but the calibration is needed to improve its low selectivity. Conventionally, principle component analysis (PCA) is used to classify the gas types, but this method has lower accuracy in some case and hard to verify in real-time. Alternatively, ANN is powerful algorithm to accurate sensing through collecting the data and training procedure and it can be verified the gas type and concentration in real-time. ROIC was fabricated in complementary metal-oxide-semiconductor (CMOS) 180-nm process and then the efficiency of the system with adaptive ROIC and ANN algorithm was experimentally verified into gas monitoring system prototype. Also, Bluetooth supports wireless connectivity to PC and mobile and pattern recognition and prediction code for SAR ADC is performed in MATLAB. Real-time gas information is monitored by Android-based application in smartphone. The dual-mode operation, optimization of performance and prediction code are adjusted with microcontroller unit (MCU). Monitoring mode is improved by x2.6 of figure-of-merits (FoM) that compared with previous resistive interface.clos