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

Investigating the Advantages of Magnetomyography in Assistive Healthcare Technology

Assistive healthcare technologies and prosthetics are crucial for individuals with muscle impairments. In 2005, the number of limb losses from trauma exceeded 700,000, projected to double by 2050, affecting approximately 1,326,000 civilians. Understanding the fundamental principles of muscle function, therefore, is key to developing innovative assistive technologies that can improve the quality of life for people with disabilities. Surface electromyography (sEMG), measuring electrical muscle activity, has long been a common tool in assistive technologies, but various obstacles have limited its widespread application. Capturing sEMG signals via the skin and subcutaneous fat poses a main challenge as they act as a low-pass filter and lead to the loss of critical information. Thus, new alternative technologies are needed to address this challenge. Magnetomyography (MMG) is a technology that can noninvasively measure magnetic muscle signals. Unlike sEMG, MMG signals are not affected by various tissues as they are transparent for magnetic signals. This paper presents the fundamental scenarios, including fat thickness on the EMG and MMG signals, with finite element (FE) simulations using COMSOL. The effects of 50-750 μ m fat on the recorded electrical and magnetic signals have been evaluated. The results indicate that by increasing fat thickness to 250μ m, the electrical signals decrease 66%, while MMG signals decline by 12%. Hence, the MMG can provide more accurate measurements of muscle activity for control strategies in prosthetic limbs

Spontaneous Muscle Activity Classification with Delay-Based Reservoir Computing

No abstract available

Investigating the Volume Conduction Effect in MMG and EMG during Action Potential Recording

The study and measurement of the magnetic field from the skeletal muscle is called Magnetomyography (MMG). These magnetic fields are produced by the same ion currents which give rise to the electrical signals that are recorded with electromyography (EMG). For non-invasive measurements, the electric properties of subcutaneous tissue, i.e., most importantly, have a strong influence on the recorded signals. This paper presents a computational model to study the volume conduction effect with the finite-difference time-domain simulations using Sim4Life. The effects of 1 mm fat on the recorded electrical and magnetic signals from the skin surface have been evaluated in both EMG and MMG. The results indicate that due to 1 mm fat, the electrical signals decrease over 60% through traveling across layers between the muscle and skin surface, while these layers are transparent to the magnetic field. In a similar simulation procedure, when the new fibers are recruited, the interference among electrical signals makes the strength of recorded signals behave non-linearly proportional to the increasing number of active muscle fibers. Sim4Life simulations show that the recorded magnetic signals do not have the same trajectory as electrical signals. Hence, the changes in EMG signals caused by volume conduction effect can result in signal misinterpretations

Wearable super-resolution muscle-machine interfacing

No abstract available