A new detection method for EMG activity monitoring

Optimized strategies for developing high-speed muscle activity monitors utilizing multi-resolution energy operator

Introduction: Electromyographic (EMG) activity monitoring constitutes the core of foundational research for the application of EMG signals in medical diagnostics, sports science, and human-machine interaction. However, the current research trend predominantly focuses on the recognition technologies of EMG signals, while the techniques for accurately detecting the onset and offset points of muscle activity-the change-point detection of EMG signals-have not received the necessary attention and thorough investigation. Methods: A novel method for EMG signal activity detection based on a variant version of the Teager-Kaiser energy operator (TKEO), namely the multi-resolution energy operator (MTEO), is proposed. Two strategies for constructing EMG activity monitors using MTEO are presented. One is a threshold-based detector (MEOTD) relying on signal baseline segment information, and the other is a detector mimicking the structure of a convolutional neural network (MEONND) without requiring prior knowledge of the signal. A semi-subjective evaluation model based on the Analytic Hierarchy Process (AHP) is used to evaluate the performance of the monitors on real EMG data. Results and discussion: The results show that the MTEO has stronger preprocessing ability for EMG signals, and that the MTEO-based monitors are more reliable and accurate. In particular, the MEONND can achieve both computational efficiency and accuracy simultaneously. The proposed method for EMG signal activity detection improves both detection quality and efficiency without increasing algorithm complexity. This method can be applied to various fields that involve EMG signal analysis, such as ergonomics, human-machine interaction, and biomedical engineering.

Impact of professional experience on clinical judgment and muscular response in various neuromuscular tests

Muscle testing is an integral component in assessing musculoskeletal function and tailoring rehabilitation efforts. This study aimed i. to identify an objective evaluation system sensitive to analyze changes in different muscular conditions in different neuromuscular tests across a spectrum of professional experience levels; and ii. to analyze differences in objective parameters and clinical judgment between participants of different levels of expertise in different muscular conditions in different neuromuscular tests. Participants included 60 subjects with Level I to III expertise who performed blinded neuromuscular tests on the middle deltoid and rectus femoris muscles of 40 volunteer subjects. The methodology centered on standardizing test protocols to minimize variability, employing EMG to quantify muscle activity, thermography to capture thermographic muscular response, and digital dynamometry to measure muscular resistance. The findings revealed that while traditional methods like thermography and electromyography provide valuable insights, digital dynamometry stands out for its sensitivity in detecting muscle condition changes in neuromuscular test. Moreover, the data underscored the pivotal role of advanced training and expertise in enhancing the precision and accuracy of neuromuscular diagnostics, since there were significant differences in objective parameters and clinical judgment between participants of different levels of expertise in the different muscular conditions in Middle deltoid and Rectus femoris neuromuscular tests analyzed, presenting higher expertise participant clinical judgment like objective validated instrument.

Comparison of different algorithms based on TKEO for EMG change point detection

OBJECTIVE

A significant challenge in surface electromyography (EMG) is the accurate identification of onset and offset of muscle activation while maintaining high real-time performance. Teager-Kaiser energy operator (TKEO) is widely used in muscle activity monitoring systems because of its computational simplicity and strong real-time performance. However, in contrast to TKEO ontology, few studies have examined how well the energy operator variants from multiple fields perform in conditioning EMG signals. This paper aims to investigate the role of the energy operator and its variants in EMG change point detection by a threshold detector.

APPROACH

To compare the stability and accuracy of TKEO and its variants for EMG change point detection, the EMG data of extensor carpi radialis longus and flexor carpi radialis were acquired from twenty participants operating a controller under normal and disturbed conditions, and EMG change point detection was performed by four energy operators and their rectified versions.

MAIN RESULTS

Based on the "standard" change points collected by the controller, the detection results were evaluated by three evaluation indexes: detection rate, F1 Score, and accuracy. The experimental results show that the multiresolution energy operator (MTEO) and the TKEO with rectified (abs-TKEO) are more suitable for EMG change point detection.

SIGNIFICANCE

This paper compared the effect of the energy operator and its variants on a threshold-based EMG change point detector. The experimental results in this paper can provide a reference for the selection of EMG signal conditioning methods to improve the detection performance of the EMG change point detector.