A Dataset of sEMG and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis

<p>Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow its identification and prevention.</p> <p>This dataset contains raw surface electromyographic (sEMG) data collected using the Delsys Trigno system, focusing on eight muscles, four per arm,&nbsp; from 13 healthy adult participants. Participants performed a series of 12 upper-body dynamic movements, consisting of 4 uni-articular and 2 complex/compound movements per arm.&nbsp; In addition to raw sEMG data, the dataset includes participants' self-reported fatigue levels.&nbsp;</p> <p><strong>Data Structure:</strong></p> <ul> <li><strong>sEMG Data.zip:</strong> Recorded in 1259 Hz, formatted as .csv.</li> <li><strong>self_perceived_fatigue_index.zip:</strong> Time-stamped fatigue ratings in 0-2 level, recorded at 50hz.</li> <li><strong>Protocol:</strong> Includes trial description, movements illustration and sampling frequencies.</li> <li><strong>Code</strong>: Jupyter Notebook file containing the base code to read and compute classic fatigue metrics such as Median Frequency and Mean Frequency.</li> <li><strong>Metadata:</strong> Includes participant anthropometrics, exercise habits and caffeine intake on the day of the trials.</li> </ul> <p>This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.</p>