Sensing Of Biosignals Research Articles

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

AbstractWearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These bio‐integrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost‐effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post‐processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine‐learning methods, while suggesting research directions for future studies.

Fog Based Sensing and Transmission Mechanisms of Vital Bio-Signals

Internet of Things (IoT) makes everything interconnected to communicate and perform tasks. Some IoT examples to quote are smart city, smart house, smart bus etc. The main focus of this paper is to make a survey and propose a system that automates bio-signal sensing in healthcare industry. The data which are recorded should be accurate and should reach the destination in time for decision making. This can be achieved by using precision microcontrollers enhanced with IoT and fog computing. Using IoT in healthcare industry makes these processes faster and more accurate. Keeping the existing systems in mind we propose system which is a combination of IoT and fog computing. The proposed system can be divided into 4 layers: data gathering, data transmission, data storage and data retrieval. Usage of proposed system in healthcare industry can improve the efficiency of transmitting vital data to healthcare centers during emergency

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

Improving Detection Accuracy of Memristor-Based Bio-Signal Sensing Platform.

Recently a novel neuronal activity sensor exploiting the intrinsic thresholded integrator capabilities of memristor devices has been proposed. Extracellular potentials captured by a standard bio-signal acquisition platform are fed into a memristive device which reacts to the input by changing its resistive state (RS) only when the signal ampitude exceeds a threshold. Thus, significant peaks in the neural signal can be stored as non-volatile changes in memristor resistive state whilst noise is effectively suppressed. However, as a memristor is subjected to increasing numbers of supra-threshold stimuli during practical operation, it accumulates (RS) changes and eventually saturates. This leads to severely redsignaluced neural activity detection capabilities. In this work we explore different signal processing and memristor operating procedure strategies in order to improve the detection rate of significant neuronal activity events. We analyse the data obtained from a single-memristive device biased with a reference neural recording and observe that performance can be improved markedly by a) increasing the frequency at which the memristor is reset to an initial resistive state where it is known to be highly responsive, b) appropriately preconditioning the input waveform through application of gain and offset in order to optimally exploit the intrinsic device behaviour. All results are validated by benchmarking obtained spike detection performance against a state-of-the-art template matching system utilising computationally-heavy, multi-dimensional, principal component analysis.

A Novel Differential Log-Companding Amplifier for Biosignal Sensing

We proposed a new method for designing the CMOS differential log-companding amplifier which achieves significant improvements in linearity, common-mode rejection ratio (CMRR), and output range. With the new nonlinear function used in the log-companding technology, this proposed amplifier has a very small total harmonic distortion (THD) and simultaneously a wide output current range. Furthermore, a differential structure with conventionally symmetrical configuration has been adopted in this novel method in order to obtain a high CMRR. Because all transistors in this amplifier operate in the weak inversion, the supply voltage and the total power consumption are significantly reduced. The novel log-companding amplifier was designed using a 0.18 μm CMOS technology. Improvements in THD, output current range, noise, and CMRR are verified using simulation data. The proposed amplifier operates from a 0.8 V supply voltage, shows a 6.3 μA maximum output current range, and has a 6 μW power consumption. The THD is less than 0.03%, the CMRR of this circuit is 74 dB, and the input referred current noise density is166.1 fA/Hz. This new method is suitable for biomedical applications such as electrocardiogram (ECG) signal acquisition.

2A2-I05 ワイドレンジ水晶荷重センサによる生体信号のカジュアルセンシング

2A2-I05 ワイドレンジ水晶荷重センサによる生体信号のカジュアルセンシング

A Natural Contact Sensor Paradigm for Nonintrusive and Real-Time Sensing of Biosignals in Human-Machine Interactions

Understanding of human cognitive and physical state is a basis for effective control or management of human-machine systems. The first step to achieve this is the development of sensors that are designed to be nonintrusive and measured in real-time. For the purposes of this paper, such sensors will be called biosensors. There are two existing paradigms for biosensors: wearable sensor and noncontact sensor. A wearable sensor had potential to be intrusive, and noncontact sensor methods may still be intrusiveness to a certain extent, while a noncontact sensor is limited in its capability of acquiring physiological signals. The overall objective of this paper is to propose a new paradigm called “natural-contact biosensor” upon a comprehensive evaluation of the existing paradigms. In this paper, the problem dimension for this new paradigm is defined, technical challenges are identified, and technologies to cope with these challenges are presented.