Soft Nanomembrane Sensors and Flexible Hybrid Bioelectronics for Wireless Quantification of Blepharospasm

Toward the Next Generation Human-Machine Interaction: Headworn Wearable Devices

Wearable devices are lightweight and portable devices worn directly on the body or integrated into the user's clothing or accessories. They are usually connected to the Internet and combined with various software applications to monitor the user's physical conditions. The latest research shows that wearable head devices, particularly those incorporating microfluidic technology, enable the monitoring of bodily fluids and physiological states. Here, we summarize the main forms, functions, and applications of head wearable devices through innovative researches in recent years. The main functions of wearable head devices are sensor monitoring, diagnosis, and even therapeutic interventions. Through this application, real-time monitoring of human physiological conditions and noninvasive treatment can be realized. Furthermore, microfluidics can realize real-time monitoring of body fluids and skin interstitial fluid, which is highly significant in medical diagnosis and has broad medical application prospects. However, despite the progress made, significant challenges persist in the integration of microfluidics into wearable devices at the current technological level. Herein, we focus on summarizing the cutting-edge applications of microfluidic contact lenses and offer insights into the burgeoning intersection between microfluidics and head-worn wearables, providing a glimpse into their future prospects.

An Exploratory, Randomized, Double-Blind Clinical Trial of Dipraglurant for Blepharospasm

BACKGROUND

Blepharospasm is treated with botulinum toxin, but obtaining satisfactory results is sometimes challenging.

OBJECTIVE

The aim is to conduct an exploratory trial of oral dipraglurant for blepharospasm.

METHODS

This study was an exploratory, phase 2a, randomized, double-blind, placebo-controlled trial of 15 participants who were assigned to receive a placebo or dipraglurant (50 or 100 mg) and assessed over 2 days, 1 and 2 hours following dosing. Outcome measures included multiple scales rated by clinicians or participants, digital video, and a wearable sensor.

RESULTS

Dipraglurant was well tolerated, with no obvious impact on any of the measurement outcomes. Power analyses suggested fewer subjects would be required for studies using a within-subject versus independent group design, especially for certain measures. Some outcome measures appeared more suitable than others.

CONCLUSION

Although dipraglurant appeared well tolerated, it did not produce a trend for clinical benefit. The results provide valuable information for planning further trials in blepharospasm. © 2024 International Parkinson and Movement Disorder Society.

Wireless Battery-free and Fully Implantable Organ Interfaces

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.

Quantitative evaluation of blinking in blepharospasm using electrooculogram-integrated smart eyeglasses

Smart eyeglasses with an integrated electrooculogram (EOG) device (JINS MEME ES_R^®, JINS Inc.) were evaluated as a quantitative diagnostic tool for blepharospasm. Participants without blepharospasm (n = 21) and patients with blepharospasm (n = 19) undertook two voluntary blinking tests (light and fast) while wearing the smart eyeglasses. Vertical (Vv) and horizontal (Vh) components were extracted from time-series voltage waveforms recorded during 30 s of the blinking tests. Two parameters, the ratio between the maximum and minimum values in the power spectrum (peak-bottom ratio, Fourier transform analysis) and the mean amplitude of the EOG waveform (peak amplitude analysis) were calculated. The mean amplitude of Vh from light and fast blinking was significantly higher in the blepharospasm group than in the control group (P  < 0.05 and P  < 0.05). Similarly, the peak-bottom ratio of Vv from light and fast blinking was significantly lower in the blepharospasm group than in the control group (P  < 0.05 and P  < 0.05). The mean amplitude of Vh and peak-bottom ratio of Vv correlated with the scores determined using the Jankovic rating scale (P  < 0.05 and P  < 0.01). Therefore, these parameters are sufficiently accurate for objective blepharospasm classification and diagnosis.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human-Machine Interfaces

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type, soft wearable electronic system with embedded stretchable electrodes, and a flexible wireless circuit to detect EOG signals for persistent HMIs. The headband with dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin-film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrate successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.

Fully Screen-Printed PI/PEG Blends Enabled Patternable Electrodes for Scalable Manufacturing of Skin-Conformal, Stretchable, Wearable Electronics

Recent advances in soft materials and nano-microfabrication have enabled the development of flexible wearable electronics. At the same time, printing technologies have been demonstrated to be efficient and compatible with polymeric materials for manufacturing wearable electronics. However, wearable device manufacturing still counts on a costly, complex, multistep, and error-prone cleanroom process. Here, we present fully screen-printable, skin-conformal electrodes for low-cost and scalable manufacturing of wearable electronics. The screen printing of the polyimide (PI) layer enables facile, low-cost, scalable, high-throughput manufacturing. PI mixed with poly(ethylene glycol) exhibits a shear-thinning behavior, significantly improving the printability of PI. The premixed Ag/AgCl ink is then used for conductive layer printing. The serpentine pattern of the screen-printed electrode accommodates natural deformation under stretching (30%) and bending conditions (180°), which are verified by computational and experimental studies. Real-time wireless electrocardiogram monitoring is also successfully demonstrated using the printed electrodes with a flexible printed circuit. The algorithm developed in this study can calculate accurate heart rates, respiratory rates, and heart rate variability metrics for arrhythmia detection.

Machine Learning for Bioelectronics on Wearable and Implantable Devices: Challenges and Potential

Bioelectronics presents a promising future in the field of embedded and implantable electronics, providing a range of functional applications, from personal health monitoring to bioactuators. However, due to the intrinsic difficulties present in producing and optimizing bioelectronics, recent research has focused on utilizing machine learning (ML) to reliably mitigate such issues and aid in process development. This review focuses on the recent developments of integrating ML into bioelectronics, aiding in a multitude of areas, such as material development, fabrication process optimization, and system integration. First, discussing how ML has aided in the material development by identifying complex relationships between process input parameters and desired outputs, such as product design. Second, examine the advancements in ML to accurately optimize fabrication precision and stability for various 3D printing technologies. Third, provide an overview of how ML can greatly assist in the analysis of complex, nonlinear relationships in data obtained from bioelectronics. Lastly, a summary of the challenges present with utilizing ML with bioelectronics and any other developments in this field. Such advancements in the field of bioelectronics and ML could hopefully build a strong foundation for this research field, promoting smart optimization together with effective use of ML to further enhance the effectiveness of such applications. Impact statement The article serves to give insight about the use of the machine learning (ML) techniques in the field of bioelectronics, since bioelectronics and ML are two distinct fields. This article allows bioelectronics researcher to get to know the latest advancement in the ML field. On the other hand, the article provides an insight to the ML researchers about how ML techniques can be useful in bioelectronics applications.

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes

Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.

A practical guide to troubleshooting pallidal deep brain stimulation issues in patients with dystonia

High frequency deep brain stimulation (DBS) of the internal portion of the globus pallidus has, in the last two decades, become a mainstream therapy for the management of medically-refractory dystonia syndromes. Such increasing uptake places an onus on movement disorder physicians to become familiar with this treatment modality, in particular optimal patient selection for the procedure and how to troubleshoot problems relating to sub-optimal efficacy and therapy-related side effects. Deep brain stimulation for dystonic conditions presents some unique challenges. For example, the frequent lack of immediate change in clinical status following stimulation alterations means that programming often relies on personal experience and local practice rather than real-time indicators of efficacy. Further, dystonia is a highly heterogeneous disorder, making the development of unifying guidelines and programming algorithms for DBS in this population difficult. Consequently, physicians may feel less confident in managing DBS for dystonia as compared to other indications e.g. Parkinson's disease. In this review, we integrate our years of personal experience of the programming of DBS systems for dystonia with a critical appraisal of the literature to produce a practical guide for troubleshooting common issues encountered in patients with dystonia treated with DBS, in the hope of improving the care for these patients.

Blepharospasm, Oromandibular Dystonia, and Meige Syndrome: Clinical and Genetic Update

Meige syndrome (MS) is cranial dystonia characterized by the combination of upper and lower cranial involvement and including binocular eyelid spasms (blepharospasm; BSP) and involuntary movements of the jaw muscles (oromandibular dystonia; OMD). The etiology and pathogenesis of this disorder of the extrapyramidal system are not well-understood. Neurologic and ophthalmic examinations often reveal no abnormalities, making diagnosis difficult and often resulting in misdiagnosis. A small proportion of patients have a family history of the disease, but to date no causative genes have been identified to date and no cure is available, although botulinum toxin A therapy effectively mitigates the symptoms and deep brain stimulation is gaining increasing attention as a viable alternative treatment option. Here we review the history and progress of research on MS, BSP, and OMD, as well as the etiology, pathology, diagnosis, and treatment.

Layer-Scale and Chip-Scale Transfer Techniques for Functional Devices and Systems: A Review

Hetero-integration of functional semiconductor layers and devices has received strong research interest from both academia and industry. While conventional techniques such as pick-and-place and wafer bonding can partially address this challenge, a variety of new layer transfer and chip-scale transfer technologies have been developed. In this review, we summarize such transfer techniques for heterogeneous integration of ultrathin semiconductor layers or chips to a receiving substrate for many applications, such as microdisplays and flexible electronics. We showed that a wide range of materials, devices, and systems with expanded functionalities and improved performance can be demonstrated by using these technologies. Finally, we give a detailed analysis of the advantages and disadvantages of these techniques, and discuss the future research directions of layer transfer and chip transfer techniques.

Fully Integrated, Stretchable, Wireless Skin-Conformal Bioelectronics for Continuous Stress Monitoring in Daily Life

Stress is one of the main causes that increase the risk of serious health problems. Recent wearable devices have been used to monitor stress levels via electrodermal activities on the skin. Although many biosensors provide adequate sensing performance, they still rely on uncomfortable, partially flexible systems with rigid electronics. These devices are mounted on either fingers or palms, which hinders a continuous signal monitoring. A fully-integrated, stretchable, wireless skin-conformal bioelectronic (referred to as "SKINTRONICS") is introduced here that integrates soft, multi-layered, nanomembrane sensors and electronics for continuous and portable stress monitoring in daily life. The all-in-one SKINTRONICS is ultrathin, highly soft, and lightweight, which overall offers an ergonomic and conformal lamination on the skin. Stretchable nanomembrane electrodes and a digital temperature sensor enable highly sensitive monitoring of galvanic skin response (GSR) and temperature. A set of comprehensive signal processing, computational modeling, and experimental study provides key aspects of device design, fabrication, and optimal placing location. Simultaneous comparison with two commercial stress monitors captures the enhanced performance of SKINTRONICS in long-term wearability, minimal noise, and skin compatibility. In vivo demonstration of continuous stress monitoring in daily life reveals the unique capability of the soft device as a real-world applicable stress monitor.