Wearable Surface Electromyography Sensor-Based Assessment of Intermuscular Time–Frequency Coherence Following Neuromuscular Electrical Stimulation Combined with Loaded Back Squats During Tennis Forehand Strokes

Objective: This study used synchronized wearable surface electromyography sensors and high-speed video to compare post-intervention intermuscular time–frequency coherence (TFC) among three pre-competition activation conditions during the tennis forehand drive and forehand slice. Methods: Thirty tennis players were randomly assigned to a loaded back squat group (Group A), a low-intensity electrical stimulation plus loaded back squat group (Group B), or a neuromuscular electrical stimulation (NMES) plus loaded back squat group (Group C), with 10 participants per group. Group B received low-intensity electrical stimulation without visible muscle contractions, whereas Group C received NMES sufficient to elicit visible muscle contractions. Following the interventions, wireless surface electromyographic sensors recorded muscle signals during both stroke techniques at 2000 Hz. A high-speed video system operating at 200 Hz was synchronized with the surface electromyography system through a transistor–transistor logic trigger to identify stroke events and segment complete movement cycles. One complete stroke cycle per participant and technique was analyzed. The sensor-derived TFC area of significant coherence (AZ) was calculated for 21 predefined muscle pairs in the alpha (8–15 Hz), beta (16–30 Hz), and gamma (31–50 Hz) bands. Ordinary or Welch’s one-way ANOVA was applied according to variance homogeneity. Benjamini–Hochberg false discovery rate (FDR) correction was performed across all 63 outcomes within each stroke technique. Significant ordinary and Welch models were followed by Bonferroni and Games–Howell comparisons, respectively. Results: During the forehand drive, eight sensor-derived TFC outcomes remained significant after FDR correction: BB–ABS beta and gamma, BB–LD alpha, BRD–ABS alpha, GM–PM beta and gamma, VM–LD beta, and BF–ABS beta (F = 10.747–75.185, partial η2 = 0.443–0.848, all q ≤ 0.003). During the forehand slice, six outcomes remained significant: BB–LD alpha, BRD–ABS gamma, GM–ABS alpha, VM–ABS alpha, GLM–ABS beta, and TA–ABS gamma (F = 9.384–47.466, partial η2 = 0.523–0.860, all q ≤ 0.019). Group C exhibited higher post-intervention AZ values than Groups A and B for all significant outcomes. No significant A–B differences were observed during the forehand drive. During the forehand slice, Group B showed a significantly higher BRD–ABS gamma-band AZ value than Group A, whereas the other five A–B comparisons were not significant. Conclusions: The synchronized sensor-based assessment identified distinct post-intervention TFC patterns across muscle pairs, frequency bands, and stroke techniques under the three activation conditions. NMES combined with loaded back squats was associated with higher AZ values in selected upper-limb–trunk and lower-limb–trunk muscle pairs. However, because no pre-intervention TFC baseline or direct stroke-performance outcomes were obtained, these sensor-derived differences cannot demonstrate within-participant increases in coherence or improved tennis performance.