Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.

https://iopscience.iop.org/article/10.1088/1361-6579/ab299e/pdf

Recent development of respiratory rate measurement technologies

Fiber Optic Shape Sensors: A comprehensive review

This work develops a thorough review of bioimpedance systems for healthcare applications. The basis and fundamentals of bioimpedance measurements are described covering issues ranging from the hardware diagrams to the configurations and designs of the electrodes and from the mathematical models that describe the frequency behavior of the bioimpedance to the sources of noise and artifacts. Bioimpedance applications such as body composition assessment, impedance cardiography (ICG), transthoracic impedance pneumography, electrical impedance tomography (EIT), and skin conductance are described and analyzed. A breakdown of recent advances and future challenges of bioimpedance is also performed, addressing topics such as transducers for biosensors and Lab-on-Chip technology, measurements in implantable systems, characterization of new parameters and substances, and novel bioimpedance applications.

/pdf/fundamentals-recent-advances-and-future-challenges-in-5bwumspaa3.pdf

Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications

A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented. Each electrode has an application specific integrated circuit (ASIC) mounted on a flexible printed circuit board, which is then wrapped inside a disposable fabric cover containing silver-coated electrodes to form the wearable belt. It is connected to a central hub that operates all the 32 ASICs. Each ASIC comprises a high-performance current driver capable of up to 6 mAp-p output, a voltage buffer for EIT and heart rate signal recording as well as contact impedance monitoring, and a sensor buffer that provides multi-parameter sensing. The ASIC was designed in a CMOS 0.35- $\mu \text{m}$ high-voltage process technology. It operates from ±9 V power supplies and occupies a total die area of 3.9 mm2. The EIT system has a bandwidth of 500 kHz and employs two parallel data acquisition channels to achieve a frame rate of 107 frames/s, the fastest wearable EIT system reported to date. Measured results show that the system has a measurement accuracy of 98.88% and a minimum EIT detectability of 0.86 $\Omega $ /frame. Its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated.

/pdf/a-high-frame-rate-wearable-eit-system-using-active-electrode-42g2q1jc11.pdf

A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring

Hand gesture recognition (HGR) serves as a fundamental way of communication and interaction for human being. While HGR can be applied in human computer interaction (HCI) to facilitate user interaction, it can also be utilized for bridging the language barrier. For instance, HGR can be utilized to recognize sign language, which is a visual language represented by hand gestures and used by the deaf and mute all over the world as a primary way of communication. Hand-crafted approach for vision-based HGR typically involves multiple stages of specialized processing, such as hand-crafted feature extraction methods, which are usually designed to deal with particular challenges specifically. Hence, the effectiveness of the system and its ability to deal with varied challenges across multiple datasets are heavily reliant on the methods being utilized. In contrast, deep learning approach such as convolutional neural network (CNN), adapts to varied challenges via supervised learning. However, attaining satisfactory generalization on unseen data is not only dependent on the architecture of the CNN, but also dependent on the quantity and variety of the training data. Therefore, a customized network architecture dubbed as enhanced densely connected convolutional neural network (EDenseNet) is proposed for vision-based hand gesture recognition. The modified transition layer in EDenseNet further strengthens feature propagation, by utilizing bottleneck layer to propagate the features being reused to all the feature maps in a bottleneck manner, and the following Conv layer smooths out the unwanted features. Differences between EDenseNet and DenseNet are discerned, and its performance gains are scrutinized in the ablation study. Furthermore, numerous data augmentation techniques are utilized to attenuate the effect of data scarcity, by increasing the quantity of training data, and enriching its variety to further improve generalization. Experiments have been carried out on multiple datasets, namely one NUS hand gesture dataset and two American Sign Language (ASL) datasets. The proposed EDenseNet obtains 98.50% average accuracy without augmented data, and 99.64% average accuracy with augmented data, outperforming other deep learning driven instances in both settings, with and without augmented data.

Hand gesture recognition via enhanced densely connected convolutional neural network

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has 16 active electrodes. Each electrode has an application-specific integrated circuit (ASIC) connected to it. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier, a digital controller, and multiplexors. The belt features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It provides intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement, providing superior common-mode rejection ratio. The ASIC was designed in a CMOS 0.35-μm high-voltage technology. The high-specification EIT belt has an image frame rate of 122 fps, a wide operating bandwidth of 1 MHz, and multi-frequency operation. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all possible channels. The image results confirmed the advantage of the new active electrode architecture and the benefit of wideband, multi-frequency EIT operation. The system successfully captured high-quality lung-respiration EIT images, breathing cycle, and heart rate. It can also provide boundary-shape information by using an array of MEMS sensors interfaced to the ASICs.

/pdf/a-122-fps-1-mhz-bandwidth-multi-frequency-wearable-eit-belt-2xf9fyk4j3.pdf

A 122 fps, 1 MHz Bandwidth Multi-Frequency Wearable EIT Belt Featuring Novel Active Electrode Architecture for Neonatal Thorax Vital Sign Monitoring

This paper presents a human–machine interface that establishes a link between the user and a hand prosthesis. It successfully uses electrical impedance tomography, a conventional bio-impedance imaging technique, using an array of electrodes contained in a wristband on the user's forearm. Using a high-performance analog front-end application specific integrated circuit (ASIC), the user's forearm inner bio-impedance redistribution is accurately assessed. These bio-signatures are strongly related to hand motions and using artificial neural networks, they can be learned so as to recognize the user's intention in real time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation with a gesture switching enabled sub-grouping method. Experiments with five subjects show that the system can achieve 98.5% accuracy with a grouping of three gestures and an accuracy of 94.4% with two sets of five gestures. The ASIC comprises a current driver with common-mode reduction capability and a current feedback instrumentation amplifier (that occupy an area of 0.07 mm2). The ASIC operates from ±1.65 V power supplies and has a minimum bio-impedance sensitivity of 12.7 mΩp-p.

/pdf/a-human-machine-interface-using-electrical-impedance-3lw0he45hy.pdf

A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

There has been considerable interest in electrical impedance tomography (EIT) to provide low-cost, radiation-free, real-time and wearable means for physiological status monitoring. To be competitive with other well-established imaging modalities, it is important to understand the requirements of the specific application and determine a suitable system design. This paper presents an overview of EIT circuits and systems including architectures, current drivers, analog front-end and demodulation circuits, with emphasis on integrated circuit implementations. Commonly used circuit topologies are detailed, and tradeoffs are discussed to aid in choosing an appropriate design based on the application and system priorities. The paper also describes a number of integrated EIT systems for biomedical applications, as well as discussing current challenges and possible future directions.

/pdf/electrical-impedance-tomography-for-biomedical-applications-kp3uxfpk7s.pdf

Electrical Impedance Tomography for Biomedical Applications: Circuits and Systems Review

This brief presents a 16-electrode electrical impedance tomography (EIT) system for hand gesture recognition. The hardware of the system is based on integrated circuits including a 12-bit high spectral purity current-steering DAC implemented in $0.18~\mu \text{m}$ CMOS technology, a current driver and an instrumentation amplifier in $0.35~\mu \text{m}$ CMOS technology. Both 2D and 3D EIT electrode arrangements were tested for hand gesture recognition. It is shown that using machine learning algorithms, eight hand gestures can be distinguished from the measured bio-impedance data with an accuracy of 97.9% when the electrodes are placed on a single wristband, and an accuracy of 99.5% with the same number of electrodes distributed on two wristbands for 3D EIT measurement. In particular 3D EIT demonstrated significant superiority in its ability to discriminate between gestures with similar muscle contractions.

/pdf/hand-gesture-recognition-using-three-dimensional-electrical-49ltmbne2g.pdf

Yu Wu

Papers

A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring

A 122 fps, 1 MHz Bandwidth Multi-Frequency Wearable EIT Belt Featuring Novel Active Electrode Architecture for Neonatal Thorax Vital Sign Monitoring

A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

Electrical Impedance Tomography for Biomedical Applications: Circuits and Systems Review

Hand Gesture Recognition Using Three-Dimensional Electrical Impedance Tomography