This work presents a high-resolution plantar pressure measurement system designed to capture detailed foot information for gait analysis. After training, the customized regression model can estimate lower body joints positions in real-time using foot pressure data. The demonstration will feature a UK size 8 insole, equipped with 253 resistive pressure sensors wirelessly connected to the server. Visitors will have the opportunity to experience walking with the insole, observing the plantar force distribution displayed and lower body joints as predicted by the model on the computer.

Gait analysis is a crucial method for evaluating and monitoring an individual's health. A critical aspect of this analysis is understanding how forces are distributed across the foot while walking. Existing plantar pressure insole systems often lack the resolution needed for detailed foot analysis. To address this, a real-time insole system is presented with 253 high-density resistive pressure sensors (4 sensors per cm2) for each foot with a wireless transfer rate of 60 Hz. In addition, our work combines the insole hardware with a custom convolutional neural network (CNNs) and long short-term memory (LSTM) model to predict six lower body joint landmark positions. The prediction achieves a coefficient of determination (R2) of 0.83 and a mean squared error (MSE) ranging from 7.0e-4 to 9.6e-4. With an inference time of 0.6 ms, this system provided accurate, high-resolution plantar foot pressures and insights into 3D joint movements in the lower body. It is a promising tool for applications in rehabilitation and sports performance optimisation.

Rehabilitation following total knee replacement (TKR) principally aims to reduce pain and restore knee muscle strength and function, which necessitates objective monitoring. Given the growing interest in wearable technologies for rehabilitation, this paper investigates the feasibility of using electrical impedance tomography (EIT) to monitor and estimate muscle activity, specifically focusing on resultant knee angle (KA) and muscle strength (MS). A data collection system was designed to simultaneously capture EIT data, kinematic and physiological reference data, namely KA and MS, followed by two experiments. Data processing methods, such as region of interest (ROI) selection and modelling of the correlation between EIT and reference data, were used to assess feasibility. The results indicate that the long short-term memory (LSTM) model achieved an R2 value of 0.97, suggesting a strong correlation between the EIT data and KA. While EIT demonstrated potential in detecting KA, it did not show effectiveness in capturing changes in muscle activity and MS during isometric contractions.

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.

Central venous catheterisation (CVC) involves the precise and challenging task of needle insertion, which can be difficult and prone to complications. This paper presents a bioimpedance (BIOZ) system for precision needle navigation in CVC. The system integrates BIOZ with a haptic feedback mechanism to assist clinicians during the catheterisation procedure. Four types of embedded needle probes were designed for high-sensitivity BIOZ sensing. The probe integrates with a compact electronic handle system for real-time BIOZ measurement and data communication, enabling real-time tissue classification and haptic feedback for optimal needle positioning. In 24 experimental trials with user participation using a phantom model, the system demonstrated a 100% success rate in venous entry, with a root mean square error (RMSE) of 0.66 mm and an 87% probability of achieving a position within ±1 mm of the vein centre. The compact and portable design ensures compatibility with clinical settings, laying a foundation for future improvements in robotic-assisted CVC and procedures alike.

This work introduces a unique bioimpedance (BIOZ) guided haptic needle technology designed to enhance central venous catheterisation (CVC) procedures. The portable system combines real-time BIOZ analysis, needle posture tracking, and a haptic device specifically developed for CVC research. By providing real-time haptic feedback and BIOZ analysis, the system significantly improves precision, ensuring a 100% success rate for vein entry. During the demonstration, visitors will first experience the challenges of CVC without assistance. Then, using the platform, they will experience how the system improves accuracy by guiding the needle into a phantom of the vein, greatly increasing the insertion success rate.

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.

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 current driver based on a current feedback structure, designed to drive hydrogel sensors. It achieves low power consumption, low common-mode signal on the load, and high current efficiency. The use of a negative unit gain buffer reduces the common-mode signal on the load arising from process variations. The current driver was designed in a 65-nm CMOS technology with a 3.3 V supply. Simulation results demonstrate a THD of 0.4% at 125 kHz, for 40 μAp-poutput current. The common-mode voltage on the load is reduced by 98.96% compared to a conventional topology using two independent drivers. The total current consumption is 60.3 μA, resulting in a current efficiency of 82.9%. The simulated output impedance is 2.76 MΩ at 125 kHz and 1.47 MΩ at 300 kHz.

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.

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.

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.

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 describes the design of an active electrode integrated circuit (IC) for a wearable electrical impedance tomography (EIT) system required for real time monitoring of neonatal lung function. The IC comprises a wideband high power current driver (up to 6 mAp-poutput current), a low noise voltage amplifier and two shape sensor buffers. The IC has been designed in a 0.35-μm CMOS technology. It operates from ±9 V power supplies and occupies a total die area of 5 mm2. Post-layout simulations are presented.