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

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.

This dataset contains raw surface electromyographic (sEMG) data collected using the Delsys Trigno system, focusing on eight muscles, four per arm,  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.  In addition to raw sEMG data, the dataset includes participants' self-reported fatigue levels. 

Data Structure:

• sEMG Data.zip: Recorded in 1259 Hz, formatted as .csv.
• self_perceived_fatigue_index.zip: Time-stamped fatigue ratings in 0-2 level, recorded at 50hz.
• Protocol: Includes trial description, movements illustration and sampling frequencies.
• Code: Jupyter Notebook file containing the base code to read and compute classic fatigue metrics such as Median Frequency and Mean Frequency.
• Metadata: Includes participant anthropometrics, exercise habits and caffeine intake on the day of the trials.

This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.