| dc.description.abstract | In this article, the classification of signals arising from the movements of the lower limb of
the leg (LLL) based on electromyography (EMG) (walking, sitting, up and down the stairs)
was carried out. In the data collection process, 25 athletes aged 15–22 were involved, and
two types of data sets (DS-dataset) were formed using FreeEMG and Biosignalsplux devices.
Six important time and frequency domain features were extracted from the EMG signals—
RMS (Root Mean Square), MAV (Mean Absolute Value), WL (Waveform Length), ZC (Zero
Crossing), MDF (Median Frequency), and SSCs (Slope Sign Changes). Several classification
algorithms were used to detect and classify movements, including RF (Random Forest),
NN (Neural Network), SVM (Support Vector Machine), k-NN (k-Nearest Neighbors), and
LR (Logistic Regression) models. Analysis of the experimental results showed that the
RF algorithm achieved the highest accuracy of 98.7% when classified with DS collected
via the Biosignalsplux device, demonstrating an advantage in terms of performance in
motion recognition. The results obtained from the open systems used in signal processing
enable real-time monitoring of athletes’ physical condition, which plays a crucial role in
accurately and rapidly determining the degree of muscle fatigue and the level of physical
stress experienced during training sessions, thereby allowing for more effective control of
performance and timely prevention of injuries. | en_US |
