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.

The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented.

This brief presents a continuous-time lowpass biquadratic cell developed from a folded-cascode OTA operating in the subthreshold region. The proposed cell has been implemented by configuring the OTA in a unity-gain feedback manner and inserting two individual capacitors into the output and internal folding nodes. This allows the proposed biquadratic cell to benefit from the high loop gain that maintains good linearity at passband frequencies well below its cutoff. As an application of this biquadratic cell, a pseudo-differential lowpass filter for electroencephalogram detection is designed and implemented in a 0.18-μ m standard CMOS technology. The custom filter occupies a silicon area of 230 μ m 300 μ m and operates from a 0.8-V dc supply with 7 nA total current consumption. Multi-chip measurements exhibit the average cut-off frequency at 151.2 Hz, input-referred noise of 35 μ Vrms, and linear range of 110 mVP. This work achieves the best FoM to date among recent relevant nano-power filters.

Electromyography (EMG) with individually unique characteristics, has emerged as a promising biometric trait. The capability to further encrypt EMG biometric patterns via distinct muscle activities (serve as a password), characterizes EMG biometrics with both a high recognition accuracy and revocability. The biometric component and the password component together form the global patterns of EMG. Previous EMG biometric verification methods directly extracted features from EMG signals to form global EMG representations with the biometric and password components entangled together. In this work, a disentanglement model was applied to disentangle the global EMG representations into password-specific and biometric-specific components in two separate latent spaces. The disentanglement model was built on a multibranch-encoder and single-decoder architecture. The two disentangled representations were learned separately by two cascaded support-vector domain description (SVDD) models. The model was trained and tested with data acquired on different days, to validate the interday robustness of our system, which is important for biometric verification using variable physiological signals. Results demonstrated that learning from disentangled representations contributes to a better EMG biometric verification performance compared with learning directly from the global representation. Our method achieved an Equal Error Rate (EER) of 0.0075 when impostors do not know the passwords. Furthermore, even when the impostors know the password, the biometric defense alone still managed to prevent intrusion with an EER of 0.1582. To the best of our knowledge, this is the first study to employ disentangled EMG representations for biometric verification.

Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are 0.957±0.02 and 0.958±0.02, respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision ≥ 0.75 and recall ≥ 0.75). Category: Early/Pre-Clinical Research.