Predictive Model for Assessing Knee Muscle Injury Risk in Athletes and Non-Athletes Using sEMG

Dataset Description

High-level explanation of dataset characteristics: This dataset includes electromyographic (EMG) signals captured using the BiTalino device. EMG signals were recorded during three conditions: rest, additional weight exercise, and squat activity. Data were collected from four young subjects aged between 20 and 24 years, including both athletes and non-athletes.

Motivations and summary of its content: The collection of this data focuses on understanding muscular responses, specifically around the anterior cruciate ligament (ACL), during different types of exercise. The information obtained aims to contribute to the development of predictive models for muscular injuries and enhance the effectiveness of training and treatment programs.

Potential use cases of the dataset: This dataset can be used to develop predictive models that assess the risk of muscular injuries around the ACL using EMG signal analysis. Additionally, it can be employed to optimize personalized training programs, monitor muscular recovery, and improve biomechanics in both athletes and non-athletes.

Methodology

Materials: For precise capture of EMG signals, a BiTalino device was used configured at a sampling frequency of 1 kHz. This device utilizes three electrodes to acquire EMG signals.

Data acquisition: Four young volunteers, two athletes, and two non-athletes aged between 20 and 24 years were recruited. EMG signals were recorded during three tests: rest, basic squats, and squats with additional weight. Standard electrode placement procedures were followed to minimize noise and ensure measurement accuracy.

Signal processing: Raw EMG data were converted to millivolts (mV) using the formula provided by BiTalino to normalize signal amplitude. Subsequently, filters were applied to remove environmental noise and highlight relevant characteristics of muscle activity.

Results

Various metrics derived from EMG signals were analyzed, including mean frequency, average peak amplitude, and average envelope amplitude. Results indicated significant differences between athletes and non-athletes in terms of muscle activation during different exercise conditions.

Conclusions

This project aims to develop predictive models based on EMG signal analysis to assess the risk of muscular injuries around the ACL. It is expected that these tools will provide a solid scientific foundation for the prevention and treatment of sports injuries, benefiting coaches, physiotherapists, and sports physicians in making informed decisions.

Source

Document where the dataset was presented: README OF THE PAPER