Multilevel Attention Mechanism for Motion Fatigue Recognition Based on sEMG and ACC Signal Fusion

Published: 9 July 2024| Version 2 | DOI: 10.17632/mgc3wmjsnz.2
Rong Chen


This study recruited 80 healthy university students (45 males and 35 females) from the university as participants, all of whom were right-handed. The recruitment criteria for participants included: normal or corrected-to-normal vision in both eyes, no history of drug abuse or psychiatric disorders, and no consumption of pain medications or psychiatric drugs in the past three months. Additionally, participants were required to have BMI values within the range. All participants were in good physical health with no history of cardiovascular diseases or severe muscle injuries. Furthermore, participants were instructed to refrain from consuming caffeine, nicotine, or alcohol, and to avoid engaging in vigorous exercise within 24 hours prior to the experiment. To ensure precise regulation of exercise power, this study utilized the Lode Corival CPET (Lode B. V., Groningen, The Netherlands) cycle ergometer for exercise testing. sEMG were made of Ag-AgCl material and arranged in a bipolar configuration with a center-to-center distance of 20 millimeters and a diameter of 1 centimeter. The Noraxon Ultium® EMG sensor recorded sEMG signals at a sampling frequency of 2000 Hz, and data collection was conducted wirelessly using the Noraxon MR3 software. ACC signals were collected using a 9-axis IMU sensor (model WT901WIFIC) at a sampling rate of 20 Hz. Additionally, the Ultima GX system was used to measure participants' ventilation (VE), oxygen consumption ( ), and carbon dioxide production ( ) during the exercise tests. Prior to conducting the experiments, calibration of all sensors and experimental equipment was performed to ensure the accuracy of the data.



Accelerometer, Electromyography