See how human musculoskeletal simulation modeling can help you identify blind spots in ice skating training!

In competitive sports, technical details often determine the success or failure of athletes. Especially in speed skating, a discipline that demands high efficiency of movement and power output, traditional training methods often rely on coaches' experience and visual observation, making it difficult to comprehensively capture the biomechanical characteristics of athletes during high-speed motion. In recent years, with the deep integration of motion capture technology and human biomechanics analysis software, athletes and coaches have been able to peek into the "black box" behind technical movements, accurately identify training blind spots, and improve athletic performance.

1. The Biomechanical Complexity of Skating Technique

Speed skating is a typical full-body coordinated sport, with its technical core lying in the efficiency of force transmission during the push-off phase, joint angle control, and muscle coordination and activation. As early as 1987 the year, de Boer and others discovered through mechanical measurements and electromyography analysis that the push-off movement of speed skaters resembles a "catapult mechanism," mainly relying on single-joint muscle groups such as the gluteus maximus and vastus medialis, while the extension range of the knee joint is limited by the ankle joint's inability to dorsiflex. [1] 。

In recent years, more studies have pointed out that although a low posture reduces air resistance, it limits muscle oxygenation capacity, leading to increased lactic acid accumulation. [2] Additionally, because curve skating is mostly counterclockwise, there is a significant asymmetrical activation of muscle groups on the left and right sides of the athlete, which can easily cause uneven muscle fatigue and even sports injuries. [3] 。

2. Motion Capture + Simulation Modeling: Unveiling the "Invisible Veil" of Technical Movements

To systematically evaluate these complex issues, relying solely on the naked eye or traditional video playback is far from sufficient. Nowadays, high-speed motion capture systems combined with human biomechanics analysis software (such as BOB human biomechanics analysis software and others) can achieve 3D reconstruction of athletes' movements, joint torque calculations, muscle activation timing analysis, and other functions.

For example, through marker tracking and inverse dynamics analysis, researchers can quantify key indicators such as ground reaction force during the skating push-off phase, knee joint angle changes, and power output distribution. A study on inline roller skaters found that the pressure distribution on the forefoot is closely related to push-off efficiency, with the forefoot bearing 80% of the total force [4] Such data directly guide the optimization of skating push-off techniques and adjustment of body forward lean angles.

Electromyography signals ( EMG ) synchronized with kinematic data can further reveal muscle coordination patterns. For example, elite athletes can achieve gluteus medius activation levels of 47% , while novices only reach 34%[5] This difference not only affects propulsion efficiency but also relates to technical stability during long-term training.

3. From Laboratory to Ice Rink: The Training Support Value of Simulation Modeling

The advantage of simulation modeling lies not only in "explanation" but also in "prediction." By constructing personalized musculoskeletal models of athletes, coaches can simulate the biomechanical effects of different technical movements on a computer and evaluate the impact of changing a joint angle or muscle activation pattern on overall performance.

For example, some software supports simulating skating movements under different speeds and slope conditions to analyze whether muscle load distribution is reasonable and whether there are "blind spots" of overuse or under-activation. This virtual training environment is especially suitable for use during off-season or rehabilitation phases to avoid repetitive injuries during actual on-ice training.

4. Future Outlook: The Arrival of the Era of Intelligent and Personalized Training

With the advancement of wearable sensors and artificial intelligence algorithms, real-time biomechanical feedback has become possible. Athletes can receive technical adjustment suggestions during training through smart glasses or earphones, such as "increase knee joint angle by ° and shift center of gravity forward." Such immediate intervention greatly improves training efficiency and movement quality. 5°重心前移”等。这种即时干预极大提升了训练效率与动作质量。

Moreover, integrated analysis frameworks combining multimodal data (such as cardiopulmonary function, metabolic responses, and neural control) are becoming research hotspots. In the future, we expect to see more personalized training programs that integrate physiological and biomechanical indicators, truly achieving "data-driven scientific training."

Conclusion

The combination of human musculoskeletal simulation modeling and motion capture technology is fundamentally changing traditional skating training methods. It is not only a tool for researchers but also a "third eye" for coaches and athletes, helping them see themselves clearly during high-speed movement, break through bottlenecks, and avoid injuries. With the reduction of technology costs and expansion of application scenarios, this technology will inevitably move from elite sports to a broader athletic population, promoting the entire skating sport to a higher level.

Data Sources:

[1] de Boer, R.W., et al. (1987). Moments of Force, Power, and Muscle Coordination in Speed-Skating. International Journal of Sports Medicine, 8(6), 371 – 378.  

[2] Wu, Z., et al. (2025). Physiological and Biomechanical Characteristics of Inline Speed Skating: A Systematic Scoping Review. Applied Sciences, 15, 7994.   

[3] Bongiorno, G., et al. (2023). Evaluation of muscle energy in isometric maintenance as an index of muscle fatigue in roller speed skating. Frontiers in Sports and Active Living, 5, 1153946.   

[4] Wu, W.L., et al. (2017). Selected plantar pressure characteristics associated with the skating performance of national in-line speed skaters. Sports Biomechanics, 16(2), 210 – 219.  

[5] Bongiorno, G., et al. (2024). Training in Roller Speed Skating: Proposal of Surface EMG and Kinematics Data for Educational Purposes. Sensors, 24, 7617.

Note: The data cited in this article are all from published academic literature. The content is authentic and reliable, intended only for reference and popular science purposes.