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.

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.

Knee functional disorders are one of the most common lower extremity non-traumatic injuries reported by cyclists. Incorrect bicycle configuration may predispose cyclist to injury but the evidence of an effect of saddle setback on knee pain remains inconclusive. The aim of this study was to determine the effect of saddle setback on knee joint forces during pedalling using a musculoskeletal modelling approach. Ten cyclists were assessed under three saddle setback conditions (range of changes in saddle position ~6 cm) while pedalling at a steady power output of 200 W and cadence of 90 rpm. A cycling musculoskeletal model was developed and knee joint forces were estimated using an inverse dynamics method associated with a static optimisation procedure. Our results indicate that moving the saddle forwards was not associated with an increase of patellofemoral joint forces. On the contrary, the tibiofemoral mean and peak compression force were 14 and 15% higher in the Backward than in the Forward condition, respectively. The peak compression force was related to neither pedal force nor quadriceps muscle force but coincided with the eccentric contraction of knee flexor muscles. These findings should benefit bike fitting practitioners and coaches in the design of specific training/rehabilitation protocols.

Besides its regulation by Union Cycliste Internationale, the evidence relating saddle setback to pedalling performance remains inconclusive. This study investigates the influence of saddle setback on pedalling effectiveness through two indexes: an index of pedalling force effectiveness and an index of pedalling work effectiveness. Eleven cyclists were assessed six saddle setback conditions while pedalling at a steady power output of 200 W and cadence of 90 rpm. A force sensor was integrated within the seat post to compute the centre of pressure on the saddle. From instrumented pedals, an index of force effectiveness (ratio between the force directed perpendicular to the crank arm and the total force applied to the pedal) and an index of work effectiveness (based on the minimisation of negative crank work) were calculated. In comparison with a forward position, sitting backward significantly decreased 5% cumulative total work, increased index of work effectiveness (84.2 ± 3.7 vs. 82.0 ± 4.7%), and increased index of force effectiveness (41.7 ± 2.9 vs. 39.9 ± 3.7 and 36.9 ± 0.7%). Thus, while it was previously reported that sitting more forward favours maximal power, this study demonstrates that it also leads to a decreased effectiveness in steady-state pedalling.

Recent discoveries in archaeology and palaeoanthropology highlight that stone tool knapping could have emerged first within the genera Australopithecus or Kenyanthropus rather than Homo. To explore the implications of this hypothesis determining the physical demands and motor control needed for performing the percussive movements during the oldest stone toolmaking technology (i.e. Lomekwian) would help. We analysed the joint angle patterns and muscle activity of a knapping expert using three stone tool replication techniques: unipolar flaking on the passive hammer (PH), bipolar (BP) flaking on the anvil, and multidirectional and multifacial flaking with free hand (FH). PH presents high levels of activity for Biceps brachii and wrist extensors and flexors. By contrast, BP and FH are characterized by high solicitation of forearm pronation. The synergy analyses depict a high muscular and kinematic coordination. Whereas the muscle pattern is very close between the techniques, the kinematic pattern is more variable, especially for PH. FH displays better muscle coordination and conversely lesser joint angle coordination. These observations suggest that the transition from anvil and hammer to freehand knapping techniques in early hominins would have been made possible by the acquisition of a behavioural repertoire producing an evolutionary advantage that gradually would have been beneficial for stone tool production.