This paper presents a multi-frequency time-variance-based Electrical Impedance Tomography (EIT) ASIC, designed to balance high-speed performance with low power consumption for biomedical applications. The ASIC leverages a dual-mode impedance readout architecture, incorporating time-to-digital demodulation at high frequencies and digital I-Q demodulation at low frequencies, which together address limitations in conventional EIT systems regarding data acquisition speed and power efficiency. The proposed system includes a current generator with an integrated auto-calibration scheme and a flexible clock generator with multiple PLLs, supporting a wide frequency range from 15.6 kHz to 1 MHz. Fabricated in a 65 nm CMOS process, the ASIC achieves a total power consumption of 2.43 mW and enables detailed multi-frequency impedance analysis while maintaining compactness and cost-effectiveness. In-vitro measurements with water tank validate its efficacy in different frequency-dependent impedance changes.

A heartbeat-powered harvester for cardiac monitoring will be demonstrated. The device harnesses mechanical vibrations from heartbeats and converts them into electrical energy to power pressure sensors, enabling real-time monitoring of heart’s health without an external powering source. The system integrates biocompatible piezoelectric flexible materials with sensors utilizing power electronics. Live demonstrations illustrate the system's feasibility for continuous intracardiac monitoring. Initial results suggest promising performance in terms of energy harvesting efficiency and biosignal acquisition accuracy for various medical and wearable devices applications.

This work presents a novel interaction method using bioimpedance (Bio-Z) to enhance virtual reality (VR) experience. The platform consists of two electrode bands (with 4 and 5 electrodes), a high-performance bioimpedance measurement system, a recurrent neural network deployed on a laptop and a VR headset. The system captures the Bio-Z features from muscle contraction and bone movement in the wrist and upper arm. After training, the regression model can predict hand grasp angle and forearm motion in both horizontal and vertical directions. This system can be used as a VR human-computer interaction (HCI) peripheral. Visitors will experience VR games using this demonstration platform.

Ingestible capsules with position tracking capabilities bring a significant advancement in the diagnosis and treatment of gastrointestinal disorders. However, the wearability of these capsule positioning systems is restricted by the weight and shape of the transmitter (Tx). To enhance the wearability and flexibility of the system, a novel positioning system using particle filter (PF) is proposed. By employing PF, the system can calculate the capsule’s position in 3D space using only a belt-shaped Tx coil, paired with a single Rx coil and an inertial measurement unit (IMU). PF iteratively updates the estimated position based on the capsule movement and changes in mutual inductance (MI). The performance of PF with different noises is analysed. Simulation results show that the system has a 22.5 × 11.5 × 27 cm3field of view (FOV), a root mean square error (RMSE) of 13.89 mm under noise.

This paper presents an optimization methodology to enhance the linearity of direct digital synthesis (DDS)-based on-chip sinusoidal current generators (SCGs) for bioimpedance measurements. After a detailed analysis of the origin of harmonics and their impact on measurement accuracy in conventional DDS-based SCGs, three different harmonic cancellation schemes are proposed. These are designed to decrease the harmonics in various applications with differing hardware requirements, significantly improving the precision and efficiency of DDS-based SCGs. The approach was implemented in a DDS-based SCG in 65 nm CMOS technology. Both simulations and measurements demonstrate a marked improvement in linearity with a figure-of-merit at least 9.4 times better than prior work. Although tailored for bioimpedance applications, the approach is also suitable for other low power, high linearity sinusoidal signal generators.

The current generator is one of the most essential parts in electrical impedance tomography (EIT) systems. State-of-art current generators suffer from the trade-off between current efficiency, linearity, output impedance and well-controlled output current level. To address these challenges, a current DAC based current generator with a fourth-order current-mode filter is proposed. The current generator was designed and simulated in a 65 nm CMOS process. By utilizing the proposed current-mode filter and the master and slave current DAC, the current generator achieves a well-controlled output current leveling with 255 tunning steps, 83.9% current efficiency, 0.11% THD at 1 mAppoutput current and 2 MΩ output impedance at 500 kHz.

This paper presents a differential T/R SPDT switch, designed in a 0.18 µm HV BCD technology for PMUTbased biomedical ultrasound systems. It incorporates a bootstrapping technique to pass high-voltage pulses and a shunt branch for improved isolation. The differential T/R switch is designed to interface with bimorph electrodes although a singleended version can also interface with conventional PZT/CMUT transducers. Post-layout simulation results show that the switch circuit exhibits 64 Ω on-resistance and -62 dB off-isolation. A figure-of-merit is proposed to compare ultrasound T/R switches. To the best of the authors’ knowledge, this differential switch is the first of its kind being reported for PMUT biomedical ultrasound systems.

Wireless power transfer with inductively coupled coils is widely used for powering implantable devices. For a continuously moving receiver coil (Rx), real-time detection of its position is important for the magnetic field distribution control. This paper proposes an Rx positioning system that uses three varying-pitch coils and applies a trilateration algorithm for positioning. It operates at a frequency of 125 kHz. The detection area is approximately 224 cm2, when using a 2 cm radius Rx. Experimental results show the position detection system has a 1 cm2resolution with an average deviation of 6.8 mm.

This work presents a wearable, eight-channel A-mode ultrasound system for hand gesture recognition and interactive gaming. The wearable system consists of a custom-built forearm bracelet with eight piezoelectric transducers (1 MHz) evenly distributed, an electronic hub housed on a bicep strap, a laptop with a bespoke machine learning algorithm and a robotic hand for actuation. The electronic hub drives the transducers to produce ultrasound pulses that will travel into the forearm muscles. The reflected ultrasound signals will vary based on the forearm muscular morphology (movement-dependent). By decoding the reflected ultrasound signals using machine learning techniques, a robotic hand can be controlled based on the user’s hand motions. In contrast to the conventional surface electromyography methods, the visitor can experience seamless control of a robotic hand and a maze navigation game via ultrasound as a novel sensing modality.

Amyotrophic Lateral Sclerosis (ALS) has been a grossly misrepresented end user group when developing coadaptive algorithms for Brain Computer Interfaces (BCI). Researchers have credited this issue to the difficulty of progressing disease in patients with ALS. This non-stationarity reduces accuracy over time. This paper introduces an online model, usable for a BCI using ALS patients data. The automatic coadaptive model effectively decodes 3 class motor imagery (MI) of the left, right hand and rest while adapting to address non-stationarities of Electroencephalography (EEG) over time caused by various factors over the study duration. Adapting Filter bank Common Spatial Pattern (FBCSP) algorithm, where we show it could enable above 70% detection of hand MI in ALS end users longitudinally, previously lacking evidence. The evaluation results demonstrate that the model achieves average accuracies of 72.6% over a 1–2 month period of usage involving 8 ALS patients. This work shows the first auto-adaptive model with ALS patient EEG data providing a stronger incentive for further investigation by setting benchmark models on longitudinal datasets contributing to the solution of multiple challenges in this field.

As of IEEE VR 2023, there have been 30 installments of the IEEE Virtual Reality conference (VR) or its predecessor, the Virtual Reality Annual International Symposium (VRAIS). As such, it seems an opportune time to reflect on the intellectual history of the conference, and by extension, the VR research community. This article uses scientometric techniques to undertake such an intellectual history, using co-word analysis and citation analysis to identify core themes and trends in VR research over time. We identify the papers that have stood the test of time, the most esteemed authors and researchers in the IEEE VR community, and the topics that have shaped our field to date.

An improved bootstrapped circuit topology is proposed for the design of a high-voltage, differential, single-pole double-throw transmit/receive switch for an ultrasound hand gesture recognition system. The differential transmit/receive switch is designed in a$0.18~\mu $m HV BCD technology. It is intended to interface with bimorph piezoelectric micromachined ultrasonic transducer electrodes, although a single-ended version can also interface with conventional ultrasound transducers. A comprehensive analysis has produced useful insights, namely the dependence of off-isolation on high-voltage input slew rate, and the dependence of common-mode rejection ratio on threshold mismatch and timing misalignment. In addition, an extended model has been developed to predict harmonic distortion through a DMOS transistor. Measured results verify the effectiveness of the design guidelines and switch operation. The switch circuit exhibits$67~\Omega $on-resistance and -63 dB off-isolation while occupying a modest area of$289~\mu $m$\times 295~\mu $m. The switch achieved a figure-of-merit with 74% improvement over the state-of-the-art. To the best of the authors’ knowledge, this differential switch is the first of its kind reported for piezoelectric micromachined ultrasonic transducer biomedical ultrasound systems.

This paper presents a fully-implantable closed-loop opto-electro stimulation interface for motor neuron disease studies, designed for experiments with freely moving rodents. A low power consumption Bluetooth data link is used to wirelessly control 64 opto-electro stimulation channels and receive neural recording data. The implant is powered by a wirelessly rechargeable lithium-ion battery, which can support 2.5 hours continuous operation with a stimulation output up to 10 mA. The battery is recharged using a QI standard wireless inductive power link, which can deliver >100mW power at a distance of 2 cm. The total size of the implant system is 29 mm × 20 mm × 13 mm. Its performance is compared with the state-of-the-art.

This paper presents a stimulator for a cardiac neuroprosthesis aiming to restore the parasympathetic control after heart transplantation. The stimulator is based on time-to- current conversion, instead of the conventional current mode digital-to-analog converter (DAC) that drives the output current mirrors. It uses a DAC based on capacitor charging to drive a power efficient voltage-to-current converter for output. The stimulator uses 1.8 V for system operation and 10 V for stimulation. The total power consumption is Istlmx 10 V + 18.4 μW during the biphasic current output, with a maximum Istimof 512 μA The stimulator was designed in CMOS 0.18 μm technology and post-layout simulations are presented.

High-density neural electrodes in microchannel interfaces require in-situ amplification of the neural signals and rejection of high-voltage stimulus pulses leaking to the channel in order to adequately detect neural signals in the presence of concurrent stimulation. This paper presents the design of an active microchannel neural interface in 0.18 μm CMOS employing neural recording and stimulation. To reduce stimulus artifacts, a novel method is proposed that disconnects the recording module during concurrent channel stimulation and automatically applies detection and reduction of stimulus artifacts from adjacent channels using a tunable filter. Simulations show that the method provides at least 54 dB artifact attenuation.

This paper presents a prototype integrated bidirectional stimulator ASIC capable of mixed opto-electro stimulation and electrophysiological signal recording. The development is part of the research into a fully implantable device for treating motor neurone disease using optogenetics and stem cell technology. The ASIC consists of 4 stimulator units, each featuring 16-channel optical and electrical stimulation using arbitrary current waveforms with an amplitude up to 16 mA and a frequency from 1.5 Hz to 50 kHz, and a recording front-end with a programmable bandwidth of 1 Hz to 4 kHz, and a programmable amplifier gain up to 74 dB. The ASIC was implemented in a 0.18μm CMOS technology. Simulated performance in stimulation and recording is presented.

This paper describes a simple and reproducible methodology towards a universal figure-of-merit (FoM) for evaluating the performance of electrical impedance tomography (EIT) systems using reconstructed images. Based on objective full-referencing and signal-to-noise ratio, the method provides a visually distinguishable hot-map and two new FoM factors, to address the issues where common electrical parameters are not directly related to the quality of EIT images. The paper describes the method with simulation results and develops a 16 electrode EIT system using an ASIC front-end for evaluation using the proposed method. The measured results show both visually and in terms of the proposed FoM factors, the impact on recorded EIT images with different current injection amplitudes.

A wearable multi-sensory platform for closed-loop optical stimulation control will be demonstrated. The system is developed for restoring hand grasp movement to patients suffering from muscle paralysis, for example, amyotrophic lateral sclerosis (ALS), by optical stimulating engrafted stem cell-derived motor neurons that reinnervate the extensor and flexor muscles. Movements induced by the stimulation will be monitored by using electrical impedance tomography (EIT) to detect the impedance variation caused by muscle contraction, and using inertial measurement units (IMUs) to measure the finger movements. Learning algorithms are employed for pattern recognition and the results are used for closed-loop stimulation control. During the live demonstration, visitors can control a multi-LED stimulator via a number of different hand movements with a wearable EIT system on their forearm and IMUs on their fingers.

A new approach for monitoring myocardium edema is introduced. It utilizes electrical impedance spectroscopy (EIS) to characterize edema tissue. The system has been tested on an isolated pig heart to distinguish the edema from non-edema tissue by a decrease of impedance for edema. The impedance reduction for left ventricular was 10 Ω at 100 kHz. The algorithm used for the demodulation in the developed EIS system is based on Goertzel filter which is utilized to replace the traditional coherent demodulation technique. Multisine excitation with 16 tones in the frequency range up to 1 MHz with more than 1000 samples was used for the measurements.

Wireless data telemetry for implantable medical devices (IMDs) has, in general, been limited to a few Mbps, and used for applications such as transmitting recordings from an implanted monitoring device, or uploading commands to an implanted stimulator. However, modern neural interfaces need to record high resolution potentials from hundreds of neurons; this requires much higher data rates. While fast wireless communication is possible using existing standards such as WiFi, power consumption demands are far too high for IMDs. Short range inductive link based telemetry, in particular impulse-based systems such as pulse-harmonic modulation (PHM), have demonstrated transfer speeds of up to 20 Mbps with a small power budget. However, these systems require complex and precise circuits, making them potentially susceptible to inter-symbol-interference. This work presents a new method named Short-range Quality-factor Modulation (SQuirM), which retains the low power consumption and high data rate of PHM, while improving the resilience of the system and simplifying the circuit design. Transmitter and receiver circuits were fabricated using 0.35 micron CMOS. The circuits were capable of transceiving data at speeds of up to 50.4 Mbps, with a BER of <; 4.5E-10, and a transmitter energy consumption of 8.11pJ/b.

A color-coded full reference signal-to-noise (FR-SNR) method is proposed as a simple and reproducible performance evaluation method for EIT systems. It will be demonstrated with a 16-electrode high-speed EIT system and a resistive phantom. The performance of the EIT system can be altered by artificially introducing non-ideal operating conditions such as low drive current, low ADC resolution, dc offset in the readout front-end and large electrode impedance. The performance under these conditions will be measured and displayed using the FR-SNR method.

A 32 channel wearable torso shape detection belt will be demonstrated. The belt is designed to measure the torso shape of a neonate and provide real-time boundary information to assist the electrical impedance tomography (EIT) system to produce high quality lung respiration images. The system is fully integrated on a flexible printed circuit board which is encapsulated in a silicon wearable cover. During the live demonstration, while EIT images are reconstructed, the boundary shape can be changed to improve the image.

This paper describes the design of fully differential sine pulse-width-modulation (SPWM) wave current generator for bioimpedance measurement applications. The current generator has been designed in a 0.18-μm CMOS technology. Its analog front-end operates from ±1.65 V and has a current consumption of Iout+ 22 μA + (fclk× 1.7 pA) where Ioutis the output current and fclkis the operating frequency. It can provide outputs from 50 μAppto 1 mAppof SPWM current up to 98 kHz with a maximum voltage compliance of ±1.25 V. Using linear current feedback, the current generator has a designed transconductance of 1 mA/V. Feedback also enables cancellation of common mode signals and a high output impedance.

This paper presents an application specific integrated circuit (ASIC) for use in a three-dimensional microchannel neural interface. The device is assembled with seven stacked ASICs with silicone microchannels instrumented in between. Each ASIC comprises tripolar electrodes for seven channels allowing recording or stimulation from any one of the 49 microchannels. The ASIC is implemented in 0.35μm high-voltage CMOS technology and occupies an active area of 4 mm2. The device has been tested demonstrating recording of 1 mV signals, and current controlled stimulation in the range of 5 μA to 500 μA (with 40 V compliance) and up to 50 kHz stimulation frequency. The device overcomes limits on numbers of connected microchannels in previous designs.

A wearable electrical impedance tomography (EIT) system for hand prosthesis motion control is demonstrated. The system captures the user's hand motion by measuring the impedance alterations caused by muscle and bone movement inside the forearm. These impedance data are sent to an artificial neural network for motion classification which is then used to manipulate a hand prosthesis. During the live demonstration, a sensor band is put on a volunteers' forearm for data acquisition. After signal processing, hand gestures learnt by the neural network can be recognized and the same hand motion can be recreated through the hand prosthesis in real-time.

This paper presents a high accuracy hand gesture recognition system based on electrical impedance tomography (EIT). The system interfaces the forearm using a wrist wrap with embedded electrodes. It measures the inner conductivity distributions caused by bone and muscle movement of the forearm in real-time and passes the data to a deep learning neural network for gesture recognition. The system has an EIT bandwidth of 500 kHz and a measured sensitivity in excess of 6.4 Ω per frame. Nineteen hand gestures are designed for recognition, and with the proposed round robin sub-grouping method, an accuracy of over 98% is achieved.

A wearable electrical impedance tomography system for lung respiratory monitoring will be demonstrated. The system features an active electrode integrated circuit (IC) fabricated in 0.35-μm CMOS technology. The IC is mounted on a flexible printed circuit board which is then embedded in a textile belt. During the live demonstration, the belt can be put on the volunteers' chest, and through a back-end signal processing hub, the system is able to provide real-time lung respiratory images on a remote computer.

This paper presents a fully implantable multi-channel neural prosthesis for epidural stimulation. The prosthesis features three telemetry-operated independent stimulators providing in total eighteen stimulation channels. The stimulator circuits were implemented in a 0.6-μm CMOS technology. The prosthesis is protected in a hermetically sealed ceramic enclosure and encapsulated in medical grade silicone rubber. In-vitro measured results with electrodes in saline are presented.

A fully implantable multi-channel neural prosthesis for epidural stimulation will be demonstrated. The prosthesis features three telemetry-operated independent stimulators providing in total eighteen stimulation channels. The stimulator circuits were implemented in a 0.6-μm CMOS technology. The prosthesis is protected in a hermetically sealed ceramic enclosure and encapsulated in medical grade silicone rubber for long-term implantation. During the live demonstration in-vitro tests with electrodes in saline will be performed with the prosthesis operated wirelessly from a remote computer.

An integrated circuit has been developed for a basic nonlinear negative feedback current driver which isolates the poles required for stability from the high frequency characteristics of the output transconductor. It provides a 1 MHz, 1 mAp-pcurrent with a phase delay of 16° into a 1kΩ load. While it can be designed as a stand-alone circuit its main use is for application in a proposed circuit using multiple copies of this basic circuit, which significantly extends its high frequency performance by substantially reducing phase delay at high frequencies. The circuit was fabricated in a 0.35-μm CMOS technology and measured results are presented. It is intended for bioimpedance applications.

A high voltage supply is necessary for implantable stimulators that need to deliver high stimulus current to load tissue and overcome large electrode impedance. A constantly high supply voltage results in unnecessary power wastage in low-voltage stimulation and is potentially dangerous at the neural tissue interface. This paper presents the authors' recent progress in developing a stimulation system with self-adapting supply based on the previous Active Books system. The improved design is able to measure the peak electrode voltage on any specified anode and feed back the information to a central hub unit. If the peak electrode voltage is significantly lower than the current supply voltage, the adjustable voltage regulator in the hub will issue a lower but high enough supply voltage to power up the stimulator chip. The circuit has been designed and simulated in a 0.6-μm HV CMOS process.

This paper describes the stimulation management unit for a multichannel vestibular neural prosthesis. The unit is designed as part of a stimulator ASIC in the implantable subsystem of the prosthesis. This digital unit provides the stimulator ASIC the ability to generate biphasic current pulses at specified amplitude, duration and pulse rate to drive electrodes in the semicircular canals. The circuit was implemented in 0.6-μm CMOS technology and post-layout simulations are presented to show its operation.

An integrated amplifier for electroneurogram (ENG) recordings from tripolar cuff electrodes is described. The amplifier is dedicated to urinary incontinence and other functional electrical stimulation (FES) applications. To remove myoelectric (EMG) interference a parallel RC network is used to balance the electrode impedances in the quasi-tripole amplifier configuration. The various ENG amplifier settings, such as resistance and capacitance trimming for the neutralization RC network, amplifier gain and filter cut-off frequencies, are controlled by an external microcontroller which communicates with the embedded SPI (Serial Port Interface) block in the amplifier. By this topology control of the ENG amplifier is executed in software allowing for the system parameters to be re-configured after implantation. The analog system blocks and details of the SPI logic are described. The amplifier was fabricated in a 3-V 0.35-mum BiCMOS process technology and preliminary measured results are reported. Input signals as low as 1muV can be reliably detected. The amplifier occupies an area of 1.5mm2and consumes about 1.4mW when configured to detect sub-microvolt neural signals.