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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.

This paper presents proof-of-principle of a miniatured low noise, low power, and high-sensitive malaria detection method based on the magnetoresistance (MR) sensor with a CMOS analog front-end (AFE) readout circuit for the detection of paramagnetic hemozoin particles. COMSOL Multiphysics® is employed for the finite-element (FEM) simulation of hemozoin particles to prove that the magnetic field generated from a multi-hemozoin particles system is within the sensing range of MR sensors. A CMOS AFE circuit is designed to convert the tiny current (approximately 60 µA) from MR sensors into a strong voltage signal able to be sampled and suppress high frequency and large amplitude noises stemming from the shift of the resultant magnetic field during the malaria diagnostic process. This CMOS AFE circuit is composed of a transimpedance amplifier (TIA) and a pair of Butterworth filters. This TIA can achieve a 98.5 dB dc gain and a 2.823 MHz bandwidth with low power consumption (375.65 µW) at a 3.3 V voltage supply and low input-referred noise (21.3857 nA/√Hz at 100 Hz). Butterworth filters can significantly reduce the high frequency and large amplitude noises caused by the unexpected shift of the magnetic field. The experimental results prove that the system provides an immediate response to samples with hemozoin particles and has the potential to achieve malaria parasite 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.

MagnetoMyoGraphy (MMG) with superconducting quantum interference devices (SQUIDs) enabled the measurement of very weak magnetic fields (femto to pico Tesla) generated from the human skeletal muscles during contraction. However, SQUIDs are bulky, costly and require working in a temperature-controlled environment, limiting wide-spread clinical use. We introduce a low-profile magnetoelectric (ME) sensor with analog frontend circuitry that has sensitivity to measure pico-Tesla MMG signals at room temperature. It comprises magnetostrictive and piezoelectric materials, FeCoSiB/AlN. Accurate device modelling and simulation are presented to predict device fabrication process comprehensively using the finite element method (FEM) in COMSOL Multiphysics®. The fabricated ME chip with its readout circuit was characterized under a dynamic geomagnetic field cancellation technique. The ME sensor experiment validate a very linear response with high sensitivities of up to 378 V/T driven at a resonance frequency of fres = 7.76 kHz. Measurements show the sensor limit of detections of down to 175 pT/Hz at resonance, which is in the range of MMG signals. Such a small-scale sensor has the potential to monitor chronic movement disorders and improve the end-user acceptance of human-machine interfaces.

Traditional point-of-care malaria diagnostic platforms lack sensitivity, the devices of them are bulky and expensive. To meet the demand for malaria diagnosis in malaria-endemic countries with low-resource, a cost-effective, easy-to-use, high-sensitivity and rapid diagnostic platform is needed. Here, we report the tunnel magnetoresistance (TMR) sensor to diagnose malaria by detecting the hemozoin. The hand-held device of TMR diagnostic platform consists of a cylindrical magnet, a TMR sensor, an Analog-Front-End circuit, and a microprocessor to display the results. The test result shows that this device can detect the synthetic hemozoin in labs and have the potential to be a point-of-care diagnosis.

Spintronic sensors, that are based on the tunnellingmagnetoresistive (TMR) effect, have been utilized in detecting low magnetic fields. However, still no computer-based model of these devices is available to integrated circuit designer to implement them in a hybrid spintronic-CMOS system. We developed a finite element method (FEM)-based model of a MgO-barrier TMR device in COMSOL Multiphysics®. The parameters of this model were extracted from the state-of-the-art fabrication and experimental data. Results were compared with respect to the model geometry and the used material. The proposed TMR sensor model offers a linear response with a high TMR ratio of 233% at 10 mV power supply. The model was exported to Cadence© Spectre to create a compact model using Verilog-A language. The developed sensor model was simulated with its analog front-end in same environment. This model provided a reliable benchmark for modelling of the future hybrid spintronic-CMOS developments.

Recent developments in spintronics materials and physics are promising to develop a new type of\ud magnetic sensors which can be embedded into the silicon chips. These neuromorphic sensing chips will\ud be designed to capture the biomagnetic signals from active biological tissue exploited as brain-machine\ud interface. They lead to machines that are able to sense and interact with the world in humanlike ways\ud and able to accelerate years of fitful advance in artificial intelligence. To detect the weak biomagnetic\ud signals, this work aims to develop a CMOS-compatible spintronic sensor based on the magnetoresistive\ud (MR) effect. As an alternative to bulky superconducting quantum interference device (SQUID) systems,\ud the miniaturised spintronic devices can be integrated with standard CMOS technologies makes it\ud possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost. Fig. 1 shows\ud the finite element method (FEM)-based simulation results of a Tunnelling-Magnetoresistive (TMR)\ud sensor with an optimal structure in COMSOL Multiphysics. The finest geometry and material are\ud demonstrated and compared with the state-of-the-art. The proposed TMR sensor achieves a linear\ud response with a high TMR ratio of 172% and sensitivity of 223 μV/Oe. The results are promising for\ud utilizing the TMR sensors in future miniaturized brain-machine interface, such as\ud Magnetoencephalography (MEG) systems for neuromorphic sensing.

No abstract available.