Combining Pose Estimation and inertial measurement tracking data for a biomechanical simulation of river wave surfing using optimal control - Weiß, Alexander; Koelewijn, Anne; Masmoudi, Ilias; Lluch, Èric (29th Annual Congress of the EUROPEAN COLLEGE OF SPORT SCIENCE, 2024)
INTRODUCTION: River wave surfing is gaining traction as a land-based alternative to traditional 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 inertial measurement unit (IMU)-based simulation approach for gait. We started to apply that to three-dimensional motions, but it is not working robustly yet (1). Recent advancements in human pose estimation however show precise spatial tracking but are prone to occlusions and changing lighting conditions and are bound to the capture volume (2). Inspired by (3), we propose integrating data from IMUs and pose estimation techniques within an optimal control framework to provide insights into both kinematics and kinetics in river wave surfing. METHODS: One participant was instructed to surf freely 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 single surf cycles were identified using the gyroscope data. From the camera recordings, we predicted planar joint angles using RTMPose (4), and from that three-dimensional joint angles through MotionBERT (5). We then created surfing simulations by solving optimal control problems (1, 6) on a 23-degree-of-freedom biomechanical model with 92 muscle-tendon units (1). In this optimal control problem, we tracked the joint angles from the camera images as well as 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 (FT) and back turns (BT) and between the front and rear stance leg within each surf cycle. RESULTS: Comparing front and back turns, we found only minor differences in peak hip (BT: 57.2°, FT: 56.4°) and knee (BT: 64.5°, FT: 65.8°) angles and hip (BT: 25.5 Nm, FT: 27.9 Nm) and knee (BT: 44.5 Nm, FT: 51.4 Nm) moments. Comparing the front and rear leg, the peak knee moment was on average 61.8% higher on the rear leg (87.8 Nm) over 9 turns; peak rear leg hip moment was only 4.5% higher (32.6 Nm). The absolute peak front leg joint angle was 18.5% higher for hip flexion (58.7°), and 8.2% higher for front leg knee flexion (69.8°). 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

INFLUENCE FROM WETSUIT ON THE ASSESSMENT OF BIOMECHANICAL PARAMETERS THROUGH IMU MEASUREMENTS - Weiß, Alexander; Lluch, Èric; Koelewijn, Anne (XXIX Congress of International Society of Biomechanics (ISB 2023), 2023)
INTRODUCTION The use of biomechanical assessments in aquatic environments is essential for understanding and improving performance in sports such as swimming, diving, and surfing. However, established methods of biomechanical analysis, such as optical motion capture (OMC) systems, are not feasible for use in environments like the open sea. Inertial motion capture (IMC) systems that combine inertial measurement units with optimal control simulation of musculoskeletal models offer a potential solution, since such a system can accurately estimate kinetics and kinematics of walking and running. However, natural aquatic environments are different than those in which IMC has been applied so far. For example, wetsuits are crucial in the ocean, and IMC sensors should be placed on the wetsuit to ensure a fixed location while practicing the sport. This could affect measurement accuracy. Therefore, we investigated the effect of wetsuits on the accuracy of IMC, by comparing the error between OMC and IMC with and without wearing a wetsuit. METHODS We conducted a pilot study with one participant, who walked and ran on a treadmill, while data was collected using both OMC and IMC. Each task was performed with and without a wetsuit. We then calculated joint angles and joint moments by solving optimal control problems for a sagittal plane musculoskeletal model. We created simulations by tracking the OMC and IMC data and evaluated the IMC system by comparing it to the simulation created with OMC data, both with and without the wetsuit. Joint angles from inverse kinematics, together with ground reaction forces, were tracked in the OMC simulations, while linear accelerations and angular velocities were tracked in the IMC simulations. Here, we calculated the correlation coefficient and root mean squared error (RMSE) of the joint angles and moments of the hips, knees, and ankles, as well as of the ground reaction forces. RESULTS AND DISCUSSION We found that joint angles, joint moments, and ground reaction forces correlated well between the wetsuit and no-wetsuit condition, while RMSEs are similar to those in. The joint moment predictions with the IMC were even more similar to OMC when wearing the wetsuit than without the wetsuit. The correlation coefficient here was 0.95 for the recording without wetsuit and 0.98 for the recording with wetsuit. So far, we only analysed running data of one participants. We will further evaluate our methods on other participants, as well as for walking. We also plan to evaluate our algorithms by analysing motions recorded in a controlled aquatic environment. CONCLUSIONS In conclusion, our pilot study provides initial evidence that wearing a wetsuit does not affect the quality of motion analysis performed with IMC, meaning that there are no adverse affects when placing inertial sensors on a wetsuit instead of on the body. Future studies should assess movements recorded in an aquatic environment, to fully explore the potential of IMC in this environment.</

Simulating surfing with optimal control: sensor fusion for biomechanical analysis - Weiß, Alexander; Masmoudi, Ilias; Lluch, Èric; Döllinger, Michael; Heinrich, Dieter; Koelewijn, Anne (Multibody System Dynamics, 2025)
Currently, no established biomechanical model exists for surfing. Musculoskeletal simulations provide valuable insights for athletes to enhance performance and prevent injuries using quantitative data such as joint angles, joint moments, and muscle forces. The dynamic nature of surfing makes biomechanics difficult to assess in a laboratory setting. Although inertial measurement units (IMUs) offer a potential solution, relying solely on IMU data in three-dimensional musculoskeletal models presents challenges, due to integration drift and noise. We present a multimodal approach combining IMU data with deep learning-based human pose estimation to simulate surfing movements. We collected data from seven experienced surfers on a river wave, providing a semicontrolled environment. Ten IMUs were placed on the participants, with cameras positioned around the wave. We defined an optimal control problem to drive three-dimensional biomechanical simulations and used an adapted contact model to represent the interaction between feet, surfboard, and water. To demonstrate the potential of our approach, we compared the kinematics and kinetics of the front and rear legs, and analyzed muscle forces. The study minimized tracking errors and established the first biomechanical model for surfing. Differences of up to 47% were found in rear and front leg joint moments and angles, with muscle forces being higher in the front leg. However, these differences were not statistically significant for our small sample. These results suggest weight distribution mainly relies on front leg muscles, while rear leg moments contribute to stabilization. Our findings showcase the high potential for biomechanical analysis in surfing performance using our approac
CRIS-Team Scopus Importer:2025-04-18
Keywords: Biomechanical modeling; Human pose estimation; Inertial measurement unit; Optimal control; Surfing