Author(s): WEISS, A., MASMOUDI, I., HEINRICH, D., KOELEWIJN, A., Institution: FRIEDRICH-ALEXANDER-UNIVERSITÄT ERLANGEN-NÜRNBERG, Country: GERMANY, Abstract-ID: 2039

INTRODUCTION:
River wave surfing is gaining traction as a land-based alternative to ocean surfing. However, its complexity and dynamics present challenges in sensor selection for biomechanical analysis, which is valuable for optimizing performance and preventing injuries. We have developed an optimal control-based approach to simulate movements based on inertial measurement units (IMUs), but it did not yet achieve robust results on three-dimensional (3D) motions (1). Human pose estimation, however, can do spatial tracking precisely, but it is limited by occlusions, changing lighting conditions, and capture volume (2). Inspired by (3), we therefore extended our simulation approach by integrating IMU data with pose estimation data. Here, we investigate if this approach can provide insights into kinematics and kinetics of river wave surfing.
METHODS:
One participant surfed back and forth in a river wave. He was equipped with ten IMUs, which were aligned to the corresponding body parts via calibration movements (1). Additionally, his movements were recorded from four viewpoints with RGB cameras, synchronized with the IMUs. Nine surf cycles, including a front and a back turn, were identified from the gyroscope data. From the camera recordings, we predicted planar joint angles using RTMPose (4), and from that 3D joint angles through MotionBERT (5). We then created surfing simulations by solving optimal control problems (1, 6) on a 3D musculoskeletal model (1). In this optimal control problem, we tracked the camera-based joint angles and the raw accelerometer and gyroscope data from the IMUs. The interaction between the board and water was adjusted from skiing simulations (6). We then analyzed the kinematic and kinetic differences in the hip and knee between front and back turns, and between the front and rear stance leg within each surf cycle.
RESULTS:
Comparing front and back turns, we found minor differences in peak hip (front: 56.4°, back: 57.2°) and knee (front: 65.8°, back: 64.5°) angles and hip (front: 27.9 Nm, back: 25.5 Nm) and knee (front: 51.4 Nm, back: 44.5 Nm) moments. The joint moments were higher in the rear leg, on average 36.9% higher in the knee (87.6 Nm vs. 64.0 Nm), and 3.5% higher in the hip (32.6 Nm vs. 31.5 Nm). The absolute peak flexion angle was 22.3% higher in the hip of the front leg (58.7° vs. 48.0°), while it was 10.8% higher in the knee (69.8° vs. 62.9°).
CONCLUSION:
Our results provide the first biomechanical simulations of river wave surfing. We found that the peak joint angles of the front leg are higher, while the joint moments are on average increased in the rear leg joints. That indicates that strength is more important for the rear leg while flexibility is an important ability for the front leg.
REFERENCES:
1) Nitschke et al., Front. Bioeng. Biotechnol., 2024
2) Zheng et al., ACM Comput. Surv., 2023
3) Pearl et al., J. Biomech., 2023
4) Jiang et al., ArXiv.org, 2023
5) Zhu et al., ArXiv.org, 2022
6) Heinrich et al., Front. Bioeng. Biotechnol., 2022