Soft Material-Enabled, Active Wireless, Thin-Film Bioelectronics for Quantitative Diagnostics of Cervical Dystonia

All-in-One, Wireless, Multi-Sensor Integrated Athlete Health Monitor for Real-Time Continuous Detection of Dehydration and Physiological Stress

Athletes are at high risk of dehydration, fatigue, and cardiac disorders due to extreme performance in often harsh environments. Despite advancements in sports training protocols, there is an urgent need for a non-invasive system capable of comprehensive health monitoring. Although a few existing wearables measure athlete's performance, they are limited by a single function, rigidity, bulkiness, and required straps and adhesives. Here, an all-in-one, multi-sensor integrated wearable system utilizing a set of nanomembrane soft sensors and electronics, enabling wireless, real-time, continuous monitoring of saliva osmolality, skin temperature, and heart functions is introduced. This system, using a soft patch and a sensor-integrated mouthguard, provides comprehensive monitoring of an athlete's hydration and physiological stress levels. A validation study in detecting real-time physiological levels shows the device's performance in capturing moments (400-500 s) of synchronized acute elevation in dehydration (350%) and physiological strain (175%) during field training sessions. Demonstration with a few human subjects highlights the system's capability to detect early signs of health abnormality, thus improving the healthcare of sports athletes.

Head movement dynamics in dystonia: a multi-centre retrospective study using visual perceptive deep learning

Dystonia is a neurological movement disorder characterised by abnormal involuntary movements and postures, particularly affecting the head and neck. However, current clinical assessment methods for dystonia rely on simplified rating scales which lack the ability to capture the intricate spatiotemporal features of dystonic phenomena, hindering clinical management and limiting understanding of the underlying neurobiology. To address this, we developed a visual perceptive deep learning framework that utilizes standard clinical videos to comprehensively evaluate and quantify disease states and the impact of therapeutic interventions, specifically deep brain stimulation. This framework overcomes the limitations of traditional rating scales and offers an efficient and accurate method that is rater-independent for evaluating and monitoring dystonia patients. To evaluate the framework, we leveraged semi-standardized clinical video data collected in three retrospective, longitudinal cohort studies across seven academic centres. We extracted static head angle excursions for clinical validation and derived kinematic variables reflecting naturalistic head dynamics to predict dystonia severity, subtype, and neuromodulation effects. The framework was also applied to a fully independent cohort of generalised dystonia patients for comparison between dystonia sub-types. Computer vision-derived measurements of head angle excursions showed a strong correlation with clinically assigned scores. Across comparisons, we identified consistent kinematic features from full video assessments encoding information critical to disease severity, subtype, and effects of neural circuit interventions, independent of static head angle deviations used in scoring. Our visual perceptive machine learning framework reveals kinematic pathosignatures of dystonia, potentially augmenting clinical management, facilitating scientific translation, and informing personalized precision neurology approaches.

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.

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.

Hold that pose: capturing cervical dystonia's head deviation severity from video

Objective

Deviated head posture is a defining characteristic of cervical dystonia (CD). Head posture severity is typically quantified with clinical rating scales such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS). Because clinical rating scales are inherently subjective, they are susceptible to variability that reduces their sensitivity as outcome measures. The variability could be circumvented with methods to measure CD head posture objectively. However, previously used objective methods require specialized equipment and have been limited to studies with a small number of cases. The objective of this study was to evaluate a novel software system—the Computational Motor Objective Rater (CMOR)—to quantify multi‐axis directionality and severity of head posture in CD using only conventional video camera recordings.

Methods

CMOR is based on computer vision and machine learning technology that captures 3D head angle from video. We used CMOR to quantify the axial patterns and severity of predominant head posture in a retrospective, cross‐sectional study of 185 patients with isolated CD recruited from 10 sites in the Dystonia Coalition.

Results

The predominant head posture involved more than one axis in 80.5% of patients and all three axes in 44.4%. CMOR's metrics for head posture severity correlated with severity ratings from movement disorders neurologists using both the TWSTRS‐2 and an adapted version of the Global Dystonia Rating Scale (rho = 0.59–0.68, all p <0.001).

Conclusions

CMOR's convergent validity with clinical rating scales and reliance upon only conventional video recordings supports its future potential for large scale multisite clinical trials.

Real-Time Functional Assay of Volumetric Muscle Loss Injured Mouse Masseter Muscles via Nanomembrane Electronics

Skeletal muscle has a remarkable regeneration capacity to recover its structure and function after injury, except for the traumatic loss of critical muscle volume, called volumetric muscle loss (VML). Although many extremity VML models have been conducted, craniofacial VML has not been well-studied due to unavailable in vivo assay tools. Here, this paper reports a wireless, noninvasive nanomembrane system that integrates skin-wearable printed sensors and electronics for real-time, continuous monitoring of VML on craniofacial muscles. The craniofacial VML model, using biopsy punch-induced masseter muscle injury, shows impaired muscle regeneration. To measure the electrophysiology of small and round masseter muscles of active mice during mastication, a wearable nanomembrane system with stretchable graphene sensors that can be laminated to the skin over target muscles is utilized. The noninvasive system provides highly sensitive electromyogram detection on masseter muscles with or without VML injury. Furthermore, it is demonstrated that the wireless sensor can monitor the recovery after transplantation surgery for craniofacial VML. Overall, the presented study shows the enormous potential of the masseter muscle VML injury model and wearable assay tool for the mechanism study and the therapeutic development of craniofacial VML.

Printed, Wireless, Soft Bioelectronics and Deep Learning Algorithm for Smart Human-Machine Interfaces

Recent advances in flexible materials and wearable electronics offer a noninvasive, high-fidelity recording of biopotentials for portable healthcare, disease diagnosis, and machine interfaces. Current device-manufacturing methods, however, still heavily rely on the conventional cleanroom microfabrication that requires expensive, time-consuming, and complicated processes. Here, we introduce an additive nanomanufacturing technology that explores a contactless direct printing of aerosol nanomaterials and polymers to fabricate stretchable sensors and multilayered wearable electronics. Computational and experimental studies prove the mechanical flexibility and reliability of soft electronics, considering direct mounting to the deformable human skin with a curvilinear surface. The dry, skin-conformal graphene biosensor, without the use of conductive gels and aggressive tapes, offers an enhanced biopotential recording on the skin and multiple uses (over ten times) with consistent measurement of electromyograms. The combination of soft bioelectronics and deep learning algorithm allows classifying six classes of muscle activities with an accuracy of over 97%, which enables wireless, real-time, continuous control of external machines such as a robotic hand and a robotic arm. Collectively, the comprehensive study of nanomaterials, flexible mechanics, system integration, and machine learning shows the potential of the printed bioelectronics for portable, smart, and persistent human-machine interfaces.

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

Blepharospasm (BL) is characterized by involuntary closures of the eyelids due to spasms of the orbicularis oculi muscle. The gold standard for clinical evaluation of BL involves visual inspection for manual rating scales. This approach is highly subjective and error prone. Unfortunately, there are currently no simple quantitative systems for accurate and objective diagnostics of BL. Here, we introduce a soft, flexible hybrid bioelectronic system that offers highly conformal, gentle lamination on the skin, while enabling wireless, quantitative detection of electrophysiological signals. Computational and experimental studies of soft materials and flexible mechanics provide a set of key fundamental design factors for a low-profile bioelectronic system. The nanomembrane soft electrodes, mounted around the eyes, are capable of accurately measuring clinical symptoms, including the frequency of blinking, the duration of eye closures during spasms, as well as combinations of blinking and spasms. The use of a deep-learning, convolutional neural network, with the bioelectronics offers objective, real-time classification of key pathological features in BL. The wearable bioelectronics outperform the conventional manual clinical rating, as shown by a pilot study with 13 patients. In vivo demonstration of the bioelectronics with these patients indicates the device as an easy-to-use solution for objective quantification of BL.