Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation.

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.

Many neurological conditions, including stroke, Parkinson’s disease, amyotrophic lateral sclerosis, and mild cognitive impairment, compromise cognitive and motor functions and may threaten autonomy in complex daily activities. Rehabilitation can therefore benefit from tasks that simultaneously engage these domains. Driving is a representative example, as it combines continuous motor control, visual processing, attention, decision making, and reaction to unexpected events. Driving simulators can provide a controlled and repeatable environment in which these cognitive and motor functions can be jointly trained, while also supporting the assessment and recovery of driving-related abilities when return to driving is clinically relevant. However, rehabilitation-oriented simulators often provide limited multisensory feedback compared with professional systems, whose size, cost, and complexity limit their transfer to clinical settings. This study presents a compact, three-degree-of-freedom dynamic driving simulator developed for rehabilitation-oriented exergames with adjustable difficulty and measurable in-game metrics. It combines a parallel motion platform, haptic steering, instrumented pedals, and a modular software architecture based on a multibody simulation engine and a graphics engine, with automated session reports. Four exergames were evaluated in 62 healthy licensed adults aged 19–30 years, with each exergame scheduled twice. Traffic Mode was assessed in a separate data-collection campaign involving 38 participants, with partial participant overlap between campaigns. Slalom showed a marked ceiling effect, whereas Linekeeping showed a graded distribution of performance. Reaction Test outcomes varied widely between participants and were strongly associated with approach speed. Participant rank order was moderately preserved across executions. The results characterize the proposed measures in a homogeneous healthy population and identify the most informative metrics and the task configurations requiring refinement. Their suitability for longitudinal monitoring in patients remains to be established.