Runners have a high probability of sports injuries due to improper landing patterns. This study aimed to investigate the effects of three different foot strike patterns on lower limb muscle activation in healthy young male university students without specialized sports training experience. Methods: Sixteen healthy male college students (age: 21 ± 1 years) participated in this study. They performed running with three different foot strike patterns: forefoot strike (FFS), midfoot strike (MFS), and rearfoot strike (RFS) at controlled speeds of 1.4–1.6 m/s. EMG signals from six lower limb muscles (vastus lateralis, vastus medialis, rectus femoris, tibialis anterior, lateral gastrocnemius, and medial gastrocnemius) during the stance phase were collected using a wireless EMG system (1000 Hz). Ensemble Empirical Mode Decomposition (EEMD) was employed to analyze the time–frequency characteristics of lower limb EMG signals and ankle joint co-activation patterns to investigate the corresponding neuromuscular control mechanisms. Statistical analyses were performed using repeated-measures ANOVA, and significance was set at p < 0.05. Results: The timing of maximum energy in lower limb muscles during the stance phase occurred earlier in RFS compared to FFS and MFS. At initial ground contact, the low-frequency component energy (below 60 Hz) of the medial gastrocnemius was significantly higher in MFS and RFS compared to FFS, while FFS exhibited significantly higher high-frequency component energy (61–200 Hz). The co-activation of ankle dorsiflexors and plantar flexors (TA/GM) was also significantly higher in MFS and RFS compared to FFS. During the 100 ms before foot contact, the low-frequency component energy (below 60 Hz) of the lateral gastrocnemius was significantly higher in MFS compared to FFS, and the degree of TA/GM co-activation was significantly higher in both MFS and RFS compared to FFS. Conclusions: The maximum frequency in lower limb muscles appeared earliest during the mid-stance phase in the rearfoot strike (RFS) pattern. Moreover, during the pre-activation and early stance phases, frequency differences were observed only in the medial gastrocnemius, with RFS showing significantly higher low-frequency power.

Dynamic muscle fatigue during repetitive movements can lead to changes in communication between the central nervous system and peripheral muscles. This study investigated these changes by examining electromyogram (EMG) characteristics from agonist and antagonist muscles during a fatiguing task. Twenty-two healthy male university students (age: 22.92 ± 2.19 years) performed heel raises until fatigue. EMG signals from lateral gastrocnemius (GL) and tibialis anterior (TA) muscles were processed using synchrosqueezed wavelet transform (SST). Root mean square (RMS), mean frequency (MF), power across frequency ranges, wavelet coherence, and co-activation ratio were computed. During the initial 80% of the task, RMS and EMG power increased for both muscles, while MF declined. In the final 20%, GL parameters stabilized, but TA showed significant decreases. Beta and gamma intermuscular coherence increased upon reaching 60% of the task. Alpha coherence and co-activation ratio remained constant. Results suggest that the central nervous system adopts a differentiated control strategy for agonist and antagonist muscles during fatigue progression. Initially, a coordinated “common drive” mechanism enhances both muscle groups’ activity. Later, despite continued increases in muscle activity, neural-muscular coupling remains stable. This asynchronous, differentiated control mechanism enhances our understanding of neuromuscular adaptations during fatigue, potentially contributing to the development of more targeted fatigue assessment and management strategies.

Propulsive power is one of the factors that determine the performance of sprint cycling. Pedaling rate is related to power output, and stiffness is associated with improving performance in athletic tasks. Purpose: to investigate the relationship between musculoarticular stiffness and pedaling rate. Methods: twenty-two healthy, untrained male volunteers (19 ± 2 years, 175 ± 6 cm, 74 ± 16 kg) were divided into two groups after their musculoarticular (MA) stiffness was tested, and these groups were the stiffness group (SG) and compliant group (CG). A 6-s maximal cycling test was conducted in four cycling modes, which were levels 5 and 10 air-resistance, and levels 3 and 7 magnetic-resistance. Peak and average cadence, peak power output (PO_peak), crank force (CF_peak), peak rate of crank force development (RCFD), and the angle of peak crank force were collected. The significance of differences between the two groups for these variables was assessed using an independent samples t-test. Pearson product–moment correlations were calculated to analyze the relationship between MA stiffness and each performance variable. Results: the SG had significantly higher peak cadence and average cadence at level 3 magnetic-resistance, peak crank force, and peak power output at level 10 air-resistance, peak rate of crank force development at levels 5 air-resistance, 10 air-resistance, and 3 magnetic-resistance (p < 0.05). MA stiffness was significantly correlated with average cadence at levels 5 and 10 air-resistance, peak crank force in all 4 modes, and RCFD and peak power output at level 10 air-resistance. There were no significant relationships between MA stiffness and the angle of peak crank force in each cycling mode. Conclusion: results indicate that participants with relatively higher MA stiffness seemed to have a higher pedaling rate during a 6-s sprint cycling in these conditions. They also performed a superior crank force and rate of crank force development, producing greater power output when sprint cycling. Optimizing cycling resistance or gear ratio to enhance both RCFD and musculotendinous stiffness may be crucial for improving sprint cycling performance.