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

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. The ASIC operates from $\pm$1.65 V power supplies, occupies an area of 0.07 mm2, and has a minimum bio-impedance sensitivity of 12.7 mΩp-p

Anatomical 3D Modeling of Upper Limb for Bio-impedance based Hand Motion Interpretation

Bio-impedance analysis (BIA) is a non-invasive way of assessing body compositions and has been recently used for hand motion interpretation using 'brute force' pattern recognition. To better promote BIA applications in human-machine interface, this paper develops an anatomically accurate 3D model towards a sound BIA recording strategy. The model is developed based on transient finite element analysis. It can be used for precise location of transcutaneous electrical stimulation to provide 3D current and potential distributions within the skin, fat, muscle, and bone layers of the upper arm, each defined by their dielectric properties. With the model, it is possible to investigate the impact of the electrode placement on the muscle when using, e.g., textile and flexible electrodes. As proof of concept for guiding the electrode placement, the electrical potential was simulated for two different electrode stimulation arrangements. The results showed that when the electrodes were shifted towards the upper arm, the electrical potential was reduced. This may be related to the anatomical layers' electric features and the distance of the electrode to the targeted muscle

Design of a CMOS Analog Front-End for Wearable A-Mode Ultrasound Hand Gesture Recognition

This paper presents a CMOS analog front-end for wearable A-mode ultrasound hand gesture recognition. This analog front-end is part of the research into using ultrasound to record and decode muscle signals with the aim of controlling a prosthetic hand as an alternative to surface electromyography. In this paper, the design of a pulser for driving piezoelectric transducers as well as a low-noise amplifier for the received echoes are presented. Simulation results show that the pulser circuit is capable of driving a 137 pF capacitive load with 30 V pulses at a frequency of 1 MHz and dissipates 142.1 mW power. The low-noise amplifier demonstrates a gain of 34 dB and an input-referred noise of 8.58 nV/√Hz at 1 MHz

A Goertzel Filter Based System for Fast Simultaneous Multi-Frequency EIS

Bioimpedance measurement is a non-invasive, radiation-free, and inexpensive method for measuring the electrical properties of biological tissues. In applications where transients occur, the commonly used swept sinewave is replaced with broadband signals such as multisine. This makes the signal generation and the extraction of the real and imaginary parts of the impedance challenging. In this brief, an alternative to traditional fast Fourier transform (FFT) or coherent demodulation is presented. Based on the Goertzel filter, this alternative is simpler and requires very few digital resources. Its robustness to the harmonic fold back phenomenon, enables simple ternary current pulses to be used for excitation. The developed digital architecture is capable of simultaneous demodulation of 16 frequencies with an accuracy of 97% and 96% on the magnitude and phase measurement respectively. Employing a ternary sequence allows the use of a low power H-bridge current driver. The analog front-end and demodulation algorithm were implemented in an ASIC using a 180-nm CMOS technology. The system was tested on an isolated pig heart distinguishing edema from non-edema tissue by impedance changes

Modeling of Transcutaneous Recording for Bio-impedance Analysis on the Upper-arm

Bio-impedance analysis (BIA) is a non-invasive way of assessing body composition. It has been recently adapted for hand motion interpretation with promising results. However, heavily relying on a large number of electrode arrays and learning algorithms, a compact and optimized BIA recording strategy was yet thoroughly investigated. This paper uses computational modeling to facilitate the design of the BIA strategy. An anatomically accurate 3D upper-hand model was developed based on transient finite elements. The model can give helpful insight into the effect of stimulating electrodes at numerous positions on the upper arm, which is otherwise challenging to investigate in practical studies. Different electrode arrangements were designed to obtain the optimal arrangement for the bio-impedance analysis on the upper - arm based on electrical potential and current density distributions over and within the volume conductor. The impedance and phase variation were recorded for different sides of the arm using a systematic procedure based on the optimal electrode arrangement. The results show that the proposed modeling can be used to guided BIA strategy

A 122 fps, 1 MHz bandwidth multi-frequency wearable EIT belt featuring novel active electrode architecture for neonatal thorax vital sign monitoring

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has sixteen active electrodes. Each has an application specific integrated circuit (ASIC) connected to an electrode. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier (IA), a digital controller and multiplexors. The wearable EIT 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 1o 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 wearable EIT belt can also detect patient position and torso shape information using a MEMS sensor interfaced to each ASIC. The system successfully captured high quality lung respiration EIT images, breathing cycle and heart rate

Time Stamp – A Novel Time-to-Digital Demodulation Method for Bioimpedance Implant Applications

Bioimpedance analysis is a noninvasive and inexpensive technology used to investigate the electrical properties of biological tissues. The analysis requires demodulation to extract the real and imaginary parts of the impedance. Conventional systems use complex architectures such as I-Q demodulation. In this paper, a very simple alternative time-to-digital demodulation method or ‘time stamp’ is proposed. It employs only three comparators to identify or stamp in the time domain, the crossing points of the excitation signal, and the measured signal. In a CMOS proof of concept design, the accuracy of impedance magnitude and phase is 97.06% and 98.81% respectively over a bandwidth of 10 kHz to 500 kHz. The effect of fractional-N synthesis is analysed for the counter-based zero crossing phase detector obtaining a finer phase resolution (0.51˚ at 500 kHz) using a counter clock frequency ( fclk = 12.5 MHz). Because of its circuit simplicity and ease of transmitting the time stamps, the method is very suited to implantable devices requiring low area and power consumption

A 122 fps, 1 MHz bandwidth multi-frequency wearable EIT belt featuring novel active electrode architecture for neonatal thorax vital sign monitoring

A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented. The belt has sixteen active electrodes. Each has an application specific integrated circuit (ASIC) connected to an electrode. The ASIC contains a fully differential current driver, a high-performance instrumentation amplifier (IA), a digital controller and multiplexors. The wearable EIT 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 1o 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 wearable EIT belt can also detect patient position and torso shape information using a MEMS sensor interfaced to each ASIC. The system successfully captured high quality lung respiration EIT images, breathing cycle and heart rate

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

A human-machine interface using electrical impedance tomography for hand prosthesis control

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 mm 2 ). The ASIC operates from ±1.65 V power supplies and has a minimum bio-impedance sensitivity of 12.7 mΩp-p