ECSS Paris 2023: CP-BM04
INTRODUCTION: The leg press is widely used to assess lower-limb strength in elite sports. While muscle activation of the lower extremities is influenced by joint angles, hand grip positioning is rarely standardized and varies across Olympic training centers despite its potential impact on maximal force output and neuromuscular activity. This study investigated, whether different hand grip positionings during the leg press affect maximal voluntary isometric force and surface EMG activity in lower and upper body muscles. METHODS: Fifteen healthy participants (9 males and 6 females; age: 24.2 ± 2.9 years; body height: 180.1 ± 7.6 cm; body mass: 75.2 ± 8.9 kg) performed maximal isometric trails of the leg press exercise on the IsoMed2000 leg press device at 90° knee flexion. Three hand grip positionings were tested in randomized order: side grips, arms crossed over the chest, and hands on the shoulder attachment. For each condition, three 5-s trials were completed, with a 1-min rest between trials and a 5-min rest between conditions. Peak force was recorded with a force plate, attached to the devices foot plate, and normalized to body mass. Surface EMG was used to record the muscle activity of the mm. vastus lateralis, rectus femoris, biceps femoris long head, latissimus dorsi, and pectoralis major. All data were sampled at 2000 Hz. EMG signals were band-pass filtered (10–500 Hz, Butterworth) and rectified, and both EMG and force data were subsequently low-pass filtered at 6 Hz. Repeated measures ANOVA were calculated to analyze the effect of the hand grip positioning on the peak force and the respective muscle activities. The level of significance was set at α = 0.05 and Bonferroni-corrected during the post-hoc tests in case of a significant ANOVA. RESULTS: Peak force did not differ significantly between hand grip positioning (crossed: 25.5 ± 2.93 N·kg⁻¹; shoulder: 25.12 ± 2.70 N·kg⁻¹; side: 26.19 ± 2.57 N·kg⁻¹; p = 0.11, η² = 0.14). Mean EMG activity of the lower-limb muscles and the latissimus dorsi showed no significant differences between conditions. In contrast, pectoralis major activation differed significantly across hand grip positioning (p = 0.002, η² = 0.38). Bonferroni-adjusted post hoc analyses revealed higher activation in the side-grip condition compared to the shoulder condition (p = 0.008, Cohen’s d = 1.08), while no other pairwise comparison reached significance. CONCLUSION: Maximal voluntary isometric force and lower body muscle activity did not differ significantly between hand positions. Pectoralis major activity was significantly higher when participants held the side grips compared to the shoulder condition. This likely reflects active pushing against the side grips, pressing the upper body into the backrest. Based on the descriptive results, the crossed-arms condition may seem favorable for standardizing testing procedures, as it minimizes upper-body involvement and promotes a more isolated assessment of lower-limb muscle strength.
Read CV Angela StockertECSS Paris 2023: CP-BM04
INTRODUCTION: Whole-body coordinated movements such as tennis strokes rely on effective temporal sequencing of segmental contributions, often described as the kinetic chain, in which forces generated against the ground are transmitted through the trunk, chest, wrist, and ultimately to the racket. Previous biomechanical studies have primarily investigated this sequencing using optical motion capture or high-speed video systems with multiple markers, which require specialized equipment and controlled laboratory environments. Consequently, assessment of temporal movement structure in practical sports settings has often depended on qualitative observation. Although wearable IMUs provide a simple alternative, it remains unclear how impact-centered temporal sequencing of multiple body segments can be extracted from a single IMU. METHODS: Acceleration data were collected during hand-fed tennis forehand strokes using a single inertial measurement unit (IMU) attached to the dominant wrist. All trials were temporally aligned to the racket–ball impact, identified from the acceleration signal and defined as time zero. The acceleration waveform was decomposed into multiple frequency bands, and envelope waveforms were extracted for each band. These envelopes were analyzed as time-varying components corresponding to the trunk, chest, wrist, and racket. The timing of each component relative to impact was examined to characterize the temporal structure of segmental contributions. Temporal patterns were compared between a skilled player and a recreational player to examine differences in movement organization. RESULTS: Decomposition of the acceleration signal revealed differences in the timing of segment-related activities before impact. Envelope waveforms corresponding to the trunk, chest, wrist, and racket exhibited activity peaks at distinct time points in the pre-impact phase. In the skilled player, trunk- and chest-related components tended to appear earlier, followed by wrist- and racket-related components closer to impact. In contrast, in the recreational player, segment-related activities were more concentrated around impact, showing less temporal separation. These results indicate that impact-centered temporal analysis using a single IMU can capture differences in pre-impact movement organization associated with skill level. CONCLUSION: This study demonstrated that impact-centered temporal analysis based on a single wrist-worn IMU can reveal differences in the temporal organization of segmental contributions before impact. This approach provides a practical framework for examining movement sequencing in real-world sports settings without complex measurement systems.
Read CV Kawori SEKINEECSS Paris 2023: CP-BM04
INTRODUCTION: Isometric wall squat has been prescribed as a simple and accessible form of lower-limb exercise. Knee joint angle can be used to prescribe wall squat positioning, as an absolute indicator of exercise intensity. However, protocol delivery varies considerably between studies, with some using goniometry and others relying on participant-adjusted knee angle positioning. While goniometry is considered gold standard for joint angle measurement, it requires two hands for alignment, anatomical landmark identification, and maintaining position during measurement. These challenges can contribute to inter-tester variability and measurement error. Establishing baseline positions using goniometry can be time-consuming and uncomfortable, particularly at acute angles in untrained populations. As limb length remains relatively consistent in healthy adults following skeletal maturity (~18 years females, ~21 years males), algorithms using anthropometric measurements may offer a standardised alternative. This study validated a novel squat height calculator algorithm against goniometry for measuring knee angle during wall squats. METHODS: Twenty-one healthy adults (12 male, 9 female; age 28.3±10.3 years) completed a single testing session. Thigh length (greater trochanter to lateral femoral epicondyle) and lower leg length (lateral tibial condyle to floor) were measured. Participants performed wall squats at five target knee angles (135°, 125°, 115°, 105°, 95°) in two conditions: (1) goniometer-adjusted positions (reference standard), and (2) algorithm-derived positions. Agreement was assessed using intraclass correlation coefficient (ICC 2,1) and Bland-Altman analysis. Systematic bias was evaluated using the Wilcoxon signed-rank test. Clinical accuracy thresholds were set at ±3° and ±5°. RESULTS: The algorithm demonstrated excellent agreement with goniometer measurements across 105 paired observations (ICC=0.995, 95%CI [0.993-0.997]; r=0.996). Mean bias was minimal (-0.31°±1.34°) with 95% limits of agreement from -2.94° to +2.31°. No proportional bias was detected across the knee angle range (r=-0.019, p=.851). The Wilcoxon signed-rank test revealed a statistically significant but clinically negligible difference (Z=-2.378, p=.017; Cohen's d=0.23). Measurement error was low (SEM=1.00°; MDC₉₅=2.80°). Clinical accuracy was 99.0% within ±3° and 100% within ±5°. Accuracy within ±3° was 95.2% at 135° and 100% at all other angles. CONCLUSION: The squat height calculator algorithm provides an accurate, practical method for prescribing knee angle-specific wall squats without specialised equipment. With excellent agreement to goniometry (ICC=0.995) and 99% clinical accuracy within ±3°, this tool addresses protocol standardisation challenges and enables reliable home-based isometric exercise prescription. Future research should evaluate its application in clinical populations and home-based exercise interventions.
Read CV Helen LlewellynECSS Paris 2023: CP-BM04