Advances in Neuromuscular Functional Rehabilitation based on High-Density Electromyography

Neuromuscular diseases, which originate from lesions of nerves or muscles, are a common and serious health problem that affects the quality of daily life of patients.

In order to improve the effectiveness of rehabilitation treatment, the etiology and pathology of such diseases need to be accurately assessed. High-density EMG information analysis is a powerful tool for addressing these issues, providing an effective means of understanding and evaluating neuromuscular disorders, as well as tailoring rehabilitation treatments with greater precision.

In achieving effective rehabilitation of neuromuscular dysfunction, neuromuscular electrophysiological signals are key indicators, which are generated by the superposition of multiple motor unit action potentials that accompany muscle contraction. Therefore, it is extremely important to accurately acquire human EMG signals.

The acquisition of EMG signals mainly includes needle electrodes and surface electrodes. Surface electrode acquisition has the advantages of non-invasiveness, non-invasive, non-invasive and easy operation, so it has irreplaceable practical value in neuromuscular function rehabilitation assessment, and provides powerful data support for rehabilitation treatment.

High-density EMG signals can provide rich and comprehensive information on neuromuscular function, which provides huge and precise data in neuromuscular function analysis and rehabilitation treatment, and strongly promotes the research in the field of rehabilitation of neuromuscular dysfunction.

Research Progress

1. Decomposition of EMG signals

EMG signal decomposition occupies an important position in high-density EMG research. Through the inverse process of EMG signal computation, this technique reduces the complex EMG signals to their basic components - motor unit action potential sequences. This process can deeply reveal the activity state of central motor neurons, thus providing an objective and accurate tool for the rehabilitation assessment of neuromuscular function, helping us understand the operation of the neuromuscular system more accurately and providing a scientific basis for rehabilitation treatment.

Study of multifunctional prosthesis control based on high-density myoelectric decomposition

From: Farina D, Vujaklija I, Sartori M, et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation[J]. Nature biomedical engineering, 2017, 1(2): 0025.

2. Dysphagia and dysphonia

Due to the limited number of electrodes and insufficient information, it is difficult to accurately locate the many small muscle groups involved in swallowing and articulation, thus affecting the evaluation and treatment effect. High-density electromyography (EMG) can be used to accurately diagnose dysphagia and dysphonia, dynamically visualise the electromyographic information of the cervical pharyngeal muscle groups to reveal the muscle activities during different articulations, and even be applied to silent speech recognition to provide precise guidance for rehabilitation treatment.

A study of swallowing disorders based on high-density electromyography

From: Zhu M, Yu B, Yang W, et al. Evaluation of normal swallowing functions by using dynamic high-density surface electromyography maps[J]. Biomedical engineering online, 2017, 16: 1-18.

3. Diagnosis and treatment of low back pain and other muscle diseases

By obtaining high-density neuromuscular electrophysiological information of the low back associated with low back pain, accurate diagnostic criteria for low back pain can be constructed, and rehabilitation intervention methods for low back pain can be designed and optimised to help improve the quality of life of patients.

A study of muscle activity in low back pain based on high-density electromyography

From: Jiang, Naifu et al. "Assessment of Lumbar Muscles Coordinated Activity Based on High-Density Surface Electromyography: A Pilot Study ." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. annual International Conference vol. 2019 (2019): 2238-2241.