Diagnosis of Muscle Fatigue Using Surface Electromyography and Analysis of Associated Factors in Type 2 Diabetic Patients with Neuropathy: A Preliminary Study

Mechanisms underlying altered neuromuscular function in people with DPN

Diabetes alters numerous physiological functions and can lead to disastrous consequences in the long term. Neuromuscular function is particularly affected and is impacted early, offering an opportunity to detect the onset of diabetes-related dysfunctions and follow the advancement of the disease. The role of physical training for counteracting the deleterious effects of diabetes is well accepted but at the same time, it appears difficult to reliably assess the effects of exercise on functional capacity in patients with diabetic peripheral neuropathy (DPN). In this paper, we will review the specific characteristics of various neuromuscular dysfunctions associated with diabetes according to the DPN presence or not, and their changes over time. We present several propositions regarding the onset of neuromuscular alterations in people with diabetes compared to people with DPN. It appears that motor unit loss and neuromuscular transmission impairment are among the main mechanisms explaining the considerable degradation of neuromuscular function in the transition from a diabetic to neuropathic state. Rate of force development and contractile properties could start to decrease with the onset of preferential type II fiber atrophy, commonly reported in people with DPN. Finally, M_max amplitude could decrease with neuromuscular fatigue only in people with DPN, reflecting the fatigue-related neuromuscular transmission impairment reported in people with DPN. In this review, we show that the different neuromuscular parameters are altered at different stages of diabetes, according to the presence of DPN or not. The precise evaluation of these parameters might participate in adapting the physical training prescription.

Application of Surface Electromyography in Exercise Fatigue: A Review

Exercise fatigue is a common physiological phenomenon in human activities. The occurrence of exercise fatigue can reduce human power output and exercise performance, and increased the risk of sports injuries. As physiological signals that are closely related to human activities, surface electromyography (sEMG) signals have been widely used in exercise fatigue assessment. Great advances have been made in the measurement and interpretation of electromyographic signals recorded on surfaces. It is a practical way to assess exercise fatigue with the use of electromyographic features. With the development of machine learning, the application of sEMG signals in human evaluation has been developed. In this article, we focused on sEMG signal processing, feature extraction, and classification in exercise fatigue. sEMG based multisource information fusion for exercise fatigue was also introduced. Finally, the development trend of exercise fatigue detection is prospected.