Assessment of Muscles Fatigue during 400-Meters Running Strategies Based on the Surface EMG Signals

Abstract

The aim of this research work is to assess the muscles fatigue of the male runner during 400 meters (m) running with three types of running strategies. The Electromyography (EMG) signals from the Rectus Femoris (RF), Biceps Femoris (BF), Gluteus Maximus (GM), Gastrocnemius Lateralis (GL), and Gastrocnemius Medialis (GMS) were collected by using bipolar electrodes from the right lower extremity’s muscles. EMG signals were collected during the run on the tartan athletic track. Five subjects (non-athletes) had run 400m with three various types of running strategies. The first type: the first 200m running 85-93% of full speed and the last 200m sprinting (full speed), second type: the first 300m running 85-93% of sprinting and the last 100m sprinting, and third type: running 85-93% of sprinting for 400m. The EMG signals were transformed to the time-frequency domain using Short Time Fourier Transform to calculate the instantaneous mean frequency (IMNF) and instantaneous median frequency (IMDF). The less index fatigues were during 1st strategy, while the RF, BF, GM, and GL muscles got recovered with IMNF and IMDF with the three strategies, and the GMS muscle has less negative regression slope value with IMNF with 1st strategy during the 4th 100m of the 400m running event. From the results, it can be concluded the running with the 1st strategy get less fatigues compared with the 2nd and 3rd strategy based on the results of time-frequency domain features (IMNF and IMDF).

Kinematics of Two Special Endurance Trials: A Methodological Contribution to 400-m Performance

“…Considering the same stage of the run (between 50 m and 100 m), the world record holder at 400 m, while breaking the record, achieved a frequency of 3.94 Hz (Miyashiro et al, 2019;Pollitt et al, 2018), which shows that the distance of 350 m is closer to the 400-m sprint than the distance of 500 m. The speed of a single running step also changed, and decreased by approximately 1.30 m/s in the final phase of the run and varied, depending on the step, from 7.72 m/s to 8.20 m/s. It can be stated that these changes were influenced by increasing fatigue (Yousif et al, 2019). Similar relationships were found in the analysis of 400-m sprints (Graubner and Nixdorf, 2009;Grgic et al, 2019).…”

“…As a result, part of the energy stored in the tissues of the lower limb is released, contributing to raising the center of gravity of the body and, thus, changes in movement (Grimshaw et al, 2010). This requires increased work of actively contracting muscles, mainly due to increasing metabolite levels (Yousif et al, 2019;Wan et al, 2017). Therefore, the duration of the entire support phase is extended.…”

Application of Surface Electromyography in Exercise Fatigue: A Review

“…Feature extraction from the sEMG signal plays an important role in the accuracy of fatigue detection. Time domain, frequency domain, time-frequency domain, and nonlinear parameters are four major types in sEMG-based signal processing ( Too et al, 2018b ; Yousif et al, 2019 ; Bukhari et al, 2020 ).…”

“…WVD is a common bilinear time-frequency distribution, which is the most common type of Cohen’s time-frequency distribution. Instantaneous frequency parameters commonly used are instantaneous mean frequency (IMNF) ( Triwiyanto et al, 2017 ) and instantaneous median frequency (IMDF) ( Yousif et al, 2019 ), which show a downward trend with the deepening of fatigue degree. Average instantaneous MF has higher stability and sensitivity than frequency-domain features.…”

“…Average instantaneous MF has higher stability and sensitivity than frequency-domain features. Yousif et al (2019) applied IMNF and IMDF to assess the muscles fatigue of the male runner during 400 m running with three types of running strategies.…”