Alignment of magnetic sensing and clinical magnetomyography.

Neuromuscular diseases are a prevalent cause of prolonged and severe suffering for patients, and with the global population aging, it is increasingly becoming a pressing concern. To assess muscle activity in NMDs, clinicians and researchers typically use electromyography (EMG), which can be either non-invasive using surface EMG, or invasive through needle EMG. Surface EMG signals have a low spatial resolution, and while the needle EMG provides a higher resolution, it can be painful for the patients, with an additional risk of infection. The pain associated with the needle EMG can pose a risk for certain patient groups, such as children. For example, children with spinal muscular atrophy (type of NMD) require regular monitoring of treatment efficacy through needle EMG; however, due to the pain caused by the procedure, clinicians often rely on a clinical assessment rather than needle EMG. Magnetomyography (MMG), the magnetic counterpart of the EMG, measures muscle activity non-invasively using magnetic signals. With super-resolution capabilities, MMG has the potential to improve spatial resolution and, in the meantime, address the limitations of EMG. This article discusses the challenges in developing magnetic sensors for MMG, including sensor design and technology advancements that allow for more specific recordings, targeting of individual motor units, and reduction of magnetic noise. In addition, we cover the motor unit behavior and activation pattern, an overview of magnetic sensing technologies, and evaluations of wearable, non-invasive magnetic sensors for MMG.

Wearable super-resolution muscle-machine interfacing.

Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

Channels and Features Identification: A Review and a Machine-Learning Based Model With Large Scale Feature Extraction for Emotions and ASD Classification.

Autism Spectrum Disorder (ASD) is characterized by impairments in social and cognitive skills, emotional disorders, anxiety, and depression. The prolonged conventional ASD diagnosis raises the sheer need for early meaningful intervention. Recently different works have proposed potential for ASD diagnosis and intervention through emotions prediction using deep neural networks (DNN) and machine learning algorithms. However, these systems lack an extensive large-scale feature extraction (LSFE) analysis through multiple benchmark data sets. LSFE analysis is required to identify and utilize the most relevant features and channels for emotion recognition and ASD prediction. Considering these challenges, for the first time, we have analyzed and evaluated an extensive feature set to select the optimal features using LSFE and feature selection algorithms (FSA). A set of up to eight most suitable channels was identified using different best-case FSA. The subject-wise importance of channels and features is also identified. The proposed method provides the best-case accuracies, precision, and recall of 95, 92, and 90%, respectively, for emotions prediction using a linear support vector machine (LSVM) classifier. It also provides the best-case accuracy, precision, and recall of 100% for ASD classification. This work utilized the largest number of benchmark data sets (5) and subjects (99) for validation reported till now in the literature. The LSVM classification algorithm proposed and utilized in this work has significantly lower complexity than the DNN, convolutional neural network (CNN), Naïve Bayes, and dynamic graph CNN used in recent ASD and emotion prediction systems.

Using the Intelligent System to Improve the Delivered Adequacy of Dialysis by Preventing Intradialytic Complications.

Acute kidney failure patients while detoxificated by hemodialysis (HD) mostly or continuously faced regular problems such as low blood pressure (hypotension), muscle cramps, nausea, or vomiting. Higher intradialytic symptom leads to low-quality HD treatment. Although more known therapeutic interventions are used to relieve the HD side effects, this study was designed to investigate how intelligent systems can make highly beneficial alterations in dialysis facilities and equipment to ease intradialytic complications and help the staff deliver high-quality treatment. A search was performed among relevant research articles based on nonpharmacological intervention methods considered to prevent adverse effects of renal replacement therapy until 2020 in the PubMed databases using the terms "intradialytic complications," "intradialytic complication interventions," "nonpharmacological interventions," "intradialytic exercises," and "adequacy calculation methods." Studies included the prevalence of intradialytic complications, different strategies with the aim of preventing complications, the outcome of intradialytic exercises on dialysis symptoms, and dialysis dose calculation methods. The results showed the incidence of hypotension varying between 5% and 30%, fatigue, muscular cramps, and vomiting as the most common complications during dialysis, which greatly affect the outcome of HD sessions. To prevent hypotension, ultrafiltration profiling, sodium modeling, low dialysate temperature, and changing the position to Trendelenburg are some strategies. Urea reduction ratio (URR), formal urea kinetic modeling (FUKM), formal single-pool urea kinetics, and online clearance monitoring (OCM) are methods for calculating the delivered dose of dialysis in which OCM is a low-cost and accessible way to monitor regularly the quality of dialysis delivered. Integration of the chair and HD machine which is in direct contact with the patient provides an intelligent system that improves the management of the dialysis session to enhance the quality of healthcare service.

Spintronic Eyeblink Gesture Sensor With Wearable Interface System.

This work presents an eyeblink system that detects magnets placed on the eyelid via integrated magnetic sensors and an analogue circuit on an eyewear frame (without a glass lens). The eyelid magnets were detected using tunnelling magnetoresistance (TMR) bridge sensors with a sensitivity of 14 mV/V/Oe and were positioned centre-right and centre-left of the eyewear frame. Each eye side has a single TMR sensor wired to a single circuit, where the signal was filtered (<0.5 Hz and >30 Hz) and amplified to detect the weak magnetic field produced by the 3-millimetre (mm) diameter and 0.5 mm thickness N42 Neodymium magnets attached to a medical tape strip, for the adult-age demographic. Each eyeblink was repeated by a trigger command (right eyeblink) followed by the appropriate command, right, left or both eyeblinks. The eyeblink gesture system has shown repeatability, resulting in blinking classification based on the analogue signal amplitude threshold. As a result, the signal can be scaled and classified as well as, integrated with a Bluetooth module in real-time. This will enable end-users to connect to various other Bluetooth enabled devices for wireless assistive technologies. The eyeblink system was tested by 14 participants via a stimuli-based game. Within an average time of 185-seconds, the system demonstrated a group mean accuracy of 72% for 40 commands. Moreover, the maximum information transfer rate (ITR) of the participants was 35.95 Bits per minute.

Neural microprobe modelling and microfabrication for improved implantation and mechanical failure mitigation.

Careful design and material selection are the most beneficial strategies to ensure successful implantation and mitigate the failure of a neural probe in the long term. In order to realize a fully flexible implantable system, the probe should be easily manipulated by neuroscientists, with the potential to bend up to 90°. This paper investigates the impact of material choice, probe geometry, and crucially, implantation angle on implantation success through finite-element method simulations in COMSOL Multiphysics followed by cleanroom microfabrication. The designs introduced in this paper were fabricated using two polyimides: (i) PI-2545 as a release layer and (ii) photodefinable HD-4110 as the probe substrate. Four different designs were microfabricated, and the implantation tests were compared between an agarose brain phantom and lamb brain samples. The probes were scanned in a 7 T PharmaScan MRI coil to investigate potential artefacts. From the simulation, a triangular base and 50 µm polymer thickness were identified as the optimum design, which produced a probe 57.7 µm thick when fabricated. The probes exhibit excellent flexibility, exemplified in three-point bending tests performed with a DAGE 4000Plus. Successful implantation is possible for a range of angles between 30° and 90°. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Cleanroom strategies for micro- and nano-fabricating flexible implantable neural electronics.

Implantable electronic neural interfaces typically take the form of probes and are made with rigid materials such as silicon and metals. These have advantages such as compatibility with integrated microchips, simple implantation and high-density nanofabrication but tend to be lacking in terms of biointegration, biocompatibility and durability due to their mechanical rigidity. This leads to damage to the device or, more importantly, the brain tissue surrounding the implant. Flexible polymer-based probes offer superior biocompatibility in terms of surface biochemistry and better matched mechanical properties. Research which aims to bring the fabrication of electronics on flexible polymer substrates to the nano-regime is remarkably sparse, despite the push for flexible consumer electronics in the last decade or so. Cleanroom-based nanofabrication techniques such as photolithography have been used as pattern transfer methods by the semiconductor industry to produce single nanometre scale devices and are now also used for making flexible circuit boards. There is still much scope for miniaturizing flexible electronics further using photolithography, bringing the possibility of nanoscale, non-invasive, high-density flexible neural interfacing. This work explores the fabrication challenges of using photolithography and complementary techniques in a cleanroom for producing flexible electronic neural probes with nanometre-scale features. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Rotating neurons for all-analog implementation of cyclic reservoir computing.

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.

Schemes for Single Electron Transistor Based on Double Quantum Dot Islands Utilizing a Graphene Nanoscroll, Carbon Nanotube and Fullerene.

The single electron transistor (SET) is a nanoscale switching device with a simple equivalent circuit. It can work very fast as it is based on the tunneling of single electrons. Its nanostructure contains a quantum dot island whose material impacts on the device operation. Carbon allotropes such as fullerene (C60), carbon nanotubes (CNTs) and graphene nanoscrolls (GNSs) can be utilized as the quantum dot island in SETs. In this study, multiple quantum dot islands such as GNS-CNT and GNS-C60 are utilized in SET devices. The currents of two counterpart devices are modeled and analyzed. The impacts of important parameters such as temperature and applied gate voltage on the current of two SETs are investigated using proposed mathematical models. Moreover, the impacts of CNT length, fullerene diameter, GNS length, and GNS spiral length and number of turns on the SET's current are explored. Additionally, the Coulomb blockade ranges (CB) of the two SETs are compared. The results reveal that the GNS-CNT SET has a lower Coulomb blockade range and a higher current than the GNS-C60 SET. Their charge stability diagrams indicate that the GNS-CNT SET has smaller Coulomb diamond areas, zero-current regions, and zero-conductance regions than the GNS-C60 SET.

Challenges to the Development of the Next Generation of Self-Reporting Cardiovascular Implantable Medical Devices.

Cardiovascular disease (CVD) is a group of heart and vasculature conditions which are the leading form of mortality worldwide. Blood vessels can become narrowed, restricting blood flow, and drive the majority of hearts attacks and strokes. Reactive surgical interventions are frequently required; including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Despite successful opening of vessels and restoration of blood flow, often in-stent restenosis (ISR) and graft failure can still occur, resulting in subsequent patient morbidity and mortality. A new generation of cardiovascular implants that have sensors and real-time monitoring capabilities are being developed to combat ISR and graft failure. Self-reporting stent/graft technology could enable precision medicine-based and predictive healthcare by detecting the earliest features of disease, even before symptoms occur. Bringing an implantable medical device with wireless electronic sensing capabilities to market is complex and often obstructive undertaking. This critical review analyses the obstacles that need to be overcome for self-reporting stents/grafts to be developed and provide a precision-medicine based healthcare for cardiovascular patients. Here we assess the latest research and technological advancement in the field, the current devices; including smart cardiovascular implantable biosensors and associated wireless data and power transfer solutions. We include an evaluation of devices that have reached clinical trials and the market potential for their end-user implementation.

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.

Flexible Piezoelectric Sensors for Miniaturized Sonomyography.

Sonomyography refers to the measurement of muscle activity with an ultrasonic transducer. It is a candidate modality for applications in diagnosis of muscle conditions, rehabilitation engineering and prosthesis control as an alternative to electromyography. We propose a mechanically-flexible piezoelectric sonomyography transducer. Simulating different components of the transducer, using COMSOL Multiphysics® software, we analyze various electromechanical parameters, such as von Mises stress and charge accumulation. Our findings on modelling of a single-element device, comprised of a PZT-5H layer of thickness 66µm, with a polymer substrate (E = 2.5 GPa), demonstrate optimal flexibility and charge accumulation for sonomyography. The addition of Polyimide and PMMA (Polymethyl methacrylate) as an acoustic matching layer and an acoustic lens, respectively, allowed for adequate energy transfer to the medium, whilst still maintaining good mechanical properties. In addition, preliminary ultrasound transmission simulations (200 kHz to 30 MHz) showed the importance of the aspect ratio of the device and how there is a need for further studies on it. The development of such a technology could be of great use within the healthcare sector, not only due to its ability to provide highly accurate and varied real-time muscle data, but also because of the range of applications that could benefit from its use.

Nanotechnology Enables Novel Modalities for Neuromodulation.

Neuromodulation is of great importance both as a fundamental neuroscience research tool for analyzing and understanding the brain function, and as a therapeutic avenue for treating brain disorders. Here, an overview of conceptual and technical progress in developing neuromodulation strategies is provided, and it is suggested that recent advances in nanotechnology are enabling novel neuromodulation modalities with less invasiveness, improved biointerfaces, deeper penetration, and higher spatiotemporal precision. The use of nanotechnology and the employment of versatile nanomaterials and nanoscale devices with tailored physical properties have led to considerable research progress. To conclude, an outlook discussing current challenges and future directions for next-generation neuromodulation modalities is presented.

The Future of Neuroscience: Flexible and Wireless Implantable Neural Electronics.

Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response. This paper presents challenges in engineering and neuroscience in developing miniaturized and biointegrated alternatives, in the form of microelectrode probes. Progress in design and topology of neural implants has shifted the goal post toward highly specific recording and stimulation, targeting small groups of neurons and reducing the foreign body response with biomimetic design principles. Implantable device design recommendations, fabrication techniques, and clinical evaluation of the impact flexible, integrated probes will have on the treatment of neurological disorders are provided in this report. The choice of biocompatible material dictates fabrication techniques as novel methods reduce the complexity of manufacture. Wireless power, the final hurdle to truly implantable neural interfaces, is discussed. These aspects are the driving force behind continued research: significant breakthroughs in any one of these areas will revolutionize the treatment of neurological disorders.

Adaptive Extreme Edge Computing for Wearable Devices.

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

A CMOS Hall sensor modeling with readout circuitry and microcontroller processing for magnetic detection.

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 µm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 µm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Detection techniques of biological and chemical Hall sensors.

Integrated magnetic Hall effect sensors have been widely used in people's daily life over the past decades, and still are gaining enormous attention from researchers to establish novel applications, especially in biochemistry and biomedical healthcare. This paper reviews, classifies, compares and concludes state-of-the-art integrated Hall magnetic sensors in terms of cost, power, area, performance and application. Current applications of the Hall sensors such as detecting magnetic nanoparticles (MNPs) labeled on biomolecule, monitoring blood pulse wave velocity, characterizing soft biological materials, controlling syringe injection rate and eye surgery by training systems, and assisting magnetic resonance imaging (MRI) will be discussed comprehensively and future applications and trends will be highlighted. This review paper will introduce Hall sensor's advantages such as simple design and technology of manufacturing, low cost, low power consumption, possibility of the miniaturizing, noninvasive and room temperature measurement, with respect to the other magnetic sensing systems and methods.