Accurate and dynamic assessment of human posture during sports activities, such as cycling, can prove useful in optimizing setup, improving performance and reducing the risk of injury. This study presents a metrological evaluation of a marker-less, multi-camera 3D human pose estimation system for cyclist posture analysis, using an optoelectronic motion capture system (OptiTrack) as ground truth. The proposed marker-less system integrates 2D keypoint detections with a Weighted Triangulation method for 3D reconstruction. The experimental protocol involved synchronized acquisition with seven monochrome cameras and OptiTrack reference data during controlled indoor cycling trials. The proposed method is evaluated in terms of Mean Per Joint Position Error, Mean Per Single Joint Position Error, Mean Absolute Joint Angle Error and Pearson Correlation Coefficient. The system achieved an overall average joint position error of 18.3 mm, while joint angle estimation errors ranged from 3.0° to 11.4°, depending on the specific joint and side of the body. The average processing time of 15 ms per frame confirms the suitability of the marker-less system for real-time applications.

Driving simulators are widely used for vehicle testing, driver training, and rehabilitation, where the quality of immersion depends largely on reproducing realistic motion sensations. However, the kinematic and dynamic limitations of motion platforms, typically based on parallel kinematic mechanisms, prevent full replication of real driving accelerations, particularly in amplitude and duration. This study addresses the challenge of generating controlled head-based force cues associated with part of the inertial loading experienced during driving, which is difficult to reproduce using the motion platform alone. A haptic device concept is proposed to generate such cues, and a feasibility study is carried out to define the specification for future implementation of a robotic system integrated with the driving simulator. Numerical simulations of a race vehicle are performed to characterize the acceleration components relevant to the proposed haptic cueing strategy. Based on these results, a mockup collaborative robotic cell is designed, developed, and coupled, as a first step, with a static simulator, featuring direct real-time control. The work lays the foundation for future industrial development and scaling of robotic haptic systems for advanced driving simulators.