The online version contains supplementary material available at 10.1007/s13534-023-00264-0.

Retinal pigmentosa (RP) patients lose vision due to the loss of photoreceptors. Retinal prostheses bypass the dead photoreceptors by electrically stimulating surviving retinal neurons, such as bipolar cells or retinal ganglion cells (RGCs). In previous studies, stimulus charge has been mainly optimized to maximize the RGC response to electrical stimulation. This study aimed to investigate the effect of amplitude and duration even under the same charge condition on eliciting RGC spikes in the wild-type and degenerate retinas. Wild-type (WT) Sprague-Dawley rats were used as the normal retinal model, and Pde6b knockout rats were used as a retinal degeneration (RD) model. Electrically-evoked RGC spikes were recorded from isolated rat retinas using an 8 8 multielectrode array. The same charge was maintained (10 or 20 nC), and electrical stimulation was applied to WT and RD retinas, adjusting the amplitude and duration of the 1st phase of biphasic pulses. In the pulse modulation of the 1st phase, high amplitude (short duration) pulses induced more RGC spikes than low amplitude (long duration) pulses. Both WT and RD retinas showed a significant reduction in the number of RGC spikes upon stimulation with lower amplitude (longer duration) pulses. In clinical trials where stimulus charges are delivered to the degenerate retina of blind patients, high amplitude (short duration) pulses would help elicit more RGC spikes.

Cervical spinal cord injury (SCI) can significantly impair an individual's hand functionality due to the disruption of nerve signals from the brain to the upper extremity. Robotic assistive hand exoskeletons have been proposed as a potential technology to facilitate improved patient rehabilitation outcomes, but few exoskeleton studies utilize standardized hand function tests and questionnaires to produce quantitative data regarding exoskeleton performance. This work presents the human subject case study evaluation of the FLEXotendon Glove-III, a 5 degree-of-freedom voice-controlled, tendon-driven soft robotic assistive hand exoskeleton for individuals with SCI. The exoskeleton system was evaluated in a case study with two individuals with SCI through two standardized hand function tests namely, the Jebsen-Taylor Hand Function Test and the Toronto Rehabilitation Institute Hand Function Test and three questionnaires (Capabilities of Upper Extremities Questionnaire, Orthotics Prosthetics Users Survey, Quebec User Evaluation of Satisfaction with Assistive Technology). Minor design changes were made to the exoskeleton: integrated fingertip force sensors to sense excessive grasp force, a quick connect system to expedite the exoskeleton glove swapping process between users, compact tendon tension sensors to measure tendon force for admittance control, and a redesigned smartphone app to encompass all aspects of exoskeleton use.

The online version contains supplementary material available at 10.1007/s13534-022-00257-5.

Organ-on-a-chip (OOC) is an emerging interdisciplinary technology that reconstitutes the structure, function, and physiology of human tissues as an alternative to conventional preclinical models for drug screening. Over the last decade, substantial progress has been made in mimicking tissue- and organ-level functions on chips through technical advances in biomaterials, stem cell engineering, microengineering, and microfluidic technologies. Structural and engineering constituents, as well as biological components, are critical factors to be considered to reconstitute the tissue function and microenvironment on chips. In this review, we highlight critical engineering technologies for reconstructing the tissue microarchitecture and dynamic spatiotemporal microenvironment in OOCs. We review the technological advances in the field of OOCs for a range of applications, including systemic analysis tools that can be integrated with OOCs, multiorgan-on-chips, and large-scale manufacturing. We then discuss the challenges and future directions for the development of advanced end-user-friendly OOC systems for a wide range of applications.

This paper proposes an efficient algorithm for automatic and optimal tuning of pulse amplitude and width for sequential parameter estimation (SPE) of the neural membrane time constant and input-output (IO) curve parameters in closed-loop electromyography-guided (EMG-guided) controllable transcranial magnetic stimulation (cTMS). The proposed SPE is performed by administering a train of optimally tuned TMS pulses and updating the estimations until a stopping rule is satisfied or the maximum number of pulses is reached. The pulse amplitude is computed by the Fisher information maximization. The pulse width is chosen by maximizing a normalized depolarization factor, which is defined to separate the optimization and tuning of the pulse amplitude and width. The normalized depolarization factor maximization identifies the critical pulse width, which is an important parameter in the identifiability analysis, without any prior neurophysiological or anatomical knowledge of the neural membrane. The effectiveness of the proposed algorithm is evaluated through simulation. The results confirm satisfactory estimation of the membrane time constant and IO curve parameters for the simulation case. By defining the stopping rule based on the satisfaction of the convergence criterion with tolerance of 0.01 for 5 consecutive times for all parameters, the IO curve parameters are estimated with 52 TMS pulses, with absolute relative estimation errors (AREs) of less than 7%. The membrane time constant is estimated with 0.67% ARE, and the pulse width value tends to the critical pulse width with 0.16% ARE with 52 TMS pulses. The results confirm that the pulse width and amplitude can be tuned optimally and automatically to estimate the membrane time constant and IO curve parameters in real-time with closed-loop EMG-guided cTMS.

Brain–machine interface (BMI) provides an alternative route for controlling an external device with one’s intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

Monitoring Mean Arterial Pressure (MAP) helps calculate the arteries' flow, resistance, and pressure. It allows doctors to check how well the blood flows through our body and reaches all major organs. Photoplethysmogram technology is gaining momentum and popularity in smart wearable devices to monitor cuff-less blood pressure (BP). However, the performance reliability of the existing PPG-based BP estimation devices is still poor. Inaccuracy in estimating systolic and diastolic blood pressure leads to an overall imprecision in resultant MAP values. Hence, there is a need for robust and reliable MAP estimation algorithms. This work exploits the moving slope features of PPG contour in its first and second derivatives that directly correlate with MAP and does not require estimating systolic and diastolic blood pressure values. The proposed approach is evaluated using two different data sets (i.e., MIMIC-I and MIMIC-II) to demonstrate the robustness and reliability of the work for personalized non-invasive BP monitoring devices to estimate MAP directly. A mean absolute error of and a standard deviation of is obtained with MIMIC-II data-set using GridSearchCV random forest regressor that outperformed most of the existing related works.

Implantable medical devices capable of monitoring hundreds to thousands of electrodes have received great attention in biomedical applications for understanding of the brain function and to treat brain diseases such as epilepsy, dystonia, and Parkinson's disease. Non-invasive neural recording modalities such as fMRI and EEGs were widely used since the 1960s, but to acquire better information, invasive modalities gained popularity. Since such invasive neural recording system requires high efficiency and low power operation, they have been implemented as integrated circuits. Many techniques have been developed and applied when designing integrated high-density neural recording architecture for better performance, higher efficiency, and lower power consumption. This paper covers general knowledge of neural signals and frequently used neural recording architectures for monitoring neural activity. For neural recording architecture, various neural recording amplifier structures are covered. In addition, several neural processing techniques, which can optimize the neural recording system, are also discussed.

Trends of medical system move from a traditional in-person visit to virtual healthcare increases demands on point-of-care devices. Because ultrasound (US) is non-invasive, the demands highlight US imaging among other imaging modalities. Thanks to the development of US transducer technology, miniaturized US with application-specific integrated circuits (ASIC) have been researched. For example, applications that require small aperture sizes such as intravascular US (IVUS) and intra-cardiac echocardiography (ICE) require integration of system-on-a-chip (SoC) on the transducer. This paper reviews circuit techniques on the transmitter (TX) and receiver (RX) of the US imaging system. As TX circuits, pulser, T/RX switch, TX beamformer, and power management circuits are discussed. State-of-the-art transducer modeling, pre-amplifier, time-gain compensation, RX beamformer, quadrature sampler, and output driver are introduced as RX circuits.