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Electromyographic (EMG) signals have been widely employed as a control signal in rehabilitation and a means of diagnosis in health care. Signal amplification and filtering is the first step in surface EMG signal processing and application systems. The characteristics of the amplifiers and filters determine the quality of EMG signals. Up until now, searching for better amplification and filtering circuit design that is able to accurately capture the features of surface EMG signals for the intended applications is still a challenging. With the fast development in computer sciences and technologies, EMG signals are expected to be used and integrated within small or even tiny intelligent, automatic, robotic, and mechatronics systems. This research focused on small size amplification and filtering circuit design for processing surface EMG signals from an upper limb and aimed to fix the amplifiers and filters inside a robotic hand with limited space to command and control the robot hand movement. The research made a study on the commonly used methodologies for EMG signal processing and circuitry design and proposed a circuit design for EMG signal amplification and filtering. High-pass filters including secondorder and fourth-order with the suppression to low frequency noises are studied. The analysis, verification and experiment showed that a second-order high-pass filter can adequately suppress the low frequency noises. The proposed amplification and filtering circuit design is able to effectively clean the noises and collect the useful surface EMG signals from an upper limb. The experiment also clearly revealed that power line interference needs to be carefully handled for higher signalnoise-ratio (SNR) as a notch-filter might cause the loss of useful signal components. Commercial computer software such as LabView and Matlab were used for data acquisition software development and data analysis.

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Contactless vital signs detection, based on the Doppler radar sensor system, has opened a great opportunity in biomedical applications. The radar sensor system can be used to provide the respiratory information of people without disturbing their comfort. This sensor system promises high accuracy in measuring breathing disorders as it escapes the touching sensors which might cause discomfort to the user and negatively affect their sleeping habits. Moreover, this sensor system does not require any special environment or depend on temperature and light conditions. In this paper, we propose a model to the end users; this model is to be built based on neural networks. Our proposed system can diagnose whether a person has a low, normal, or high breathing rate. This model can also be extended to more specific categories to help doctors to determine breathing disorders in patients. In this paper, a continuous wave radar sensor system, based on a vector network analyzer (VNA), is used to measure the breathing rate remotely. The measured signal from this radar sensor system is then processed for further purposes. Different extracted feature methods are implemented to obtain the breathing rate from the non-contact radar sensor system. A model based on the machine learning technique is investigated to classify the breathing disorder. A total of 31 people who were asked to perform low/normal/high breathing were measured by the CW radar sensor. The measured data were also used to build a machine learning based model. The breathing rate measured by the CW radar sensor system is compared with the reference measurement by the five-point touching Shimmer sensor. The results of the breathing rate are compatible. Two main time-frequency (TF) extraction feature methods, short-time Fourier transform (STFT) and continuous wavelet transform (CWT), were implemented in the proposed system. Under these extraction techniques, some classification approaches were employed and have shown high accuracy in categorizing the respiratory types. The research shows the possibility of building an artificial intelligence (AI) module for a non-contact radar sensor system to inform the end user of their breathing situation. This research enables a smarter and more friendly remote-detecting vital signs sensor system.

A computer postprocessing technique is developed to remove MRI artifact arising from unknown translational motion in the imaging plane. Based on previous artifact correction methods, the improved technique uses two successive steps to reduce read out and phase-encoding direction artifacts: First, the spectrum shift method is applied to remove read-out axis translational motion. Then, the phase retrieval method is employed to eliminate the remaining subpixel motion of the read-out axis and the entire motion of the phase-encoding axis. In the presence of noise, to protect edge detection (in the spectrum shift method), two high-density gray-level markers are added, one to each side of the imaging object. Experimental results with an actual MR scan confirmed the ability of the method to correct the artifact of an MR image caused by unknown translational motion in the imaging plane.

This paper presents a survey of recent developments using Doppler radar sensor in searching and locating an alive person under debris or behind a wall. Locating a human and detecting the vital signs such as breathing rate and heartbeat using a microwave sensor is a non-invasive technique. Recently, many hardware structures, signal processing approaches, and integrated systems have been introduced by researchers in this field. The purpose is to enhance the accuracy of vital signs’ detection and location detection and reduce energy consumption. This work concentrates on the representative research on sensing systems that can find alive people under rubble when an earthquake or other disasters occur. In this paper, various operating principles and system architectures for finding survivors using the microwave radar sensors are reviewed. A comparison between these systems is also discussed.

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