Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 2: Accuracy.

Two-dimensional deep-learning pose estimation algorithms can suffer from biases in joint pose localizations, which are reflected in triangulated coordinates, and then in 3D joint angle estimation. Pose2Sim, our robust markerless kinematics workflow, comes with a physically consistent OpenSim skeletal model, meant to mitigate these errors. Its accuracy was concurrently validated against a reference marker-based method. Lower-limb joint angles were estimated over three tasks (walking, running, and cycling) performed multiple times by one participant. When averaged over all joint angles, the coefficient of multiple correlation (CMC) remained above 0.9 in the sagittal plane, except for the hip in running, which suffered from a systematic 15° offset (CMC = 0.65), and for the ankle in cycling, which was partially occluded (CMC = 0.75). When averaged over all joint angles and all degrees of freedom, mean errors were 3.0°, 4.1°, and 4.0°, in walking, running, and cycling, respectively; and range of motion errors were 2.7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.

What Makes Parkour Unique? A Narrative Review Across Miscellaneous Academic Fields.

Parkour is a growing sport that mostly involves jumping, vaulting over obstacles, and climbing in a non-dedicated setting. The authors gathered all known relevant literature across miscellaneous academic fields in order to define parkour with regard to other sports disciplines. Parkour is a lifestyle sport, and as such provides an alternative to mainstream sports, away from strict rules, standardized settings, and necessary competitions. Traceurs (parkour adepts) consider the city as a playground and as an outlet for their creativity, but they also have a strong taste for hard and individualized challenges. They usually train on non-specific structures, at ground level. Although their social background is not clear, they are mostly young and male. Traceurs are stronger than recreational athletes, especially in eccentric exercises. However, their endurance skills may be below average. One of the core specificities of parkour is its precision constraint at landing, which turns a standing long jump into a precision jump, regulated in flight so as to prepare for landing. The running precision jump follows the same landing pattern, and its flight phase contrasts with long jump techniques. Injuries, which are not more frequent than in other sports, often occur at landing and to lower limb extremities. This risk is mitigated by targeting the landing area with the forefoot instead of letting the heel hit the ground like in gymnastics, or with rolling in order to dissipate the impact. Overall, parkour focuses on adaptability to new environments, which leads to specific techniques that have not yet been extensively addressed by the literature.

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics-Part 1: Robustness.

Being able to capture relevant information about elite athletes' movement "in the wild" is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7° and 3.2° for all conditions and tasks, and mean absolute errors (compared to the reference condition-Ref) ranged between 0.35° and 1.6°. For walking, errors in the sagittal plane were: 1.5°, 0.90°, 0.19° for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.