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Quite recently, considerable attention has been paid to the concept of prediction of manual wheelchair locomotion by a predictive model simulation, with the aim of facilitating the synthesis of the biomechanics of propulsion movement and improving the wheelchair ergonomics. Generally, the biomechanical modeling of wheelchair propulsion is a highly nonlinear that depends on the upper limb motion and the contact force between the human hand and the handrim, which will increase the complexity of implementing these models to evaluate with numerical simulation the biomechanics of propulsion. In this context, a new approach for the propulsion motion prediction of a manual wheelchair based on nonlinear model predictive control (NMPC) has been proposed. Based only on the kinematics of the hand on the handrim and the dynamics of the rear wheels, this approach is able to predict the optimal hand contact on the handrim, during the push phase and the optimal hand trajectory during the recovery phase. Thus, for a given speed configuration, the predictive model simulation is able to generate the most suitable propulsion scheme. The advantage of using a simple model of the hand-wheel interaction, could allow for an easier implementation while providing a fully predictive simulation of the wheelchair propulsion. Simulation results are presented, showing the effectiveness of the proposed approach.

Sitting posture is different in subjects with spinal cord injury (SCI) compared to asymptomatic subjects. This difference comes from the spine which, without muscular activity will bend under the weight of the trunk. The impact of this human deformation is particularly visible through the stability maintenance exercise. Indeed, in response to a disturbance, subjects with a spinal cord injury will adopt new stabilization strategies through the action of the upper limbs in order to compensate for the absence of voluntary control below the complete injury level. Understanding the effects of this injury on postural control leads us to design a biomechanical model of sitting stability. With this model, it is possible to estimate human joint torques using an unknown input observer (UIO). The nonlinear structure of the model is considered via quasi-LPV framework also called Takagi-Sugeno framework. The convergence of the UIO is expressed by linear matrix inequalities (LMI), which are solved via convex optimization techniques. We provide in this paper numerical simulations and real data experiments performed in a discrete framework to demonstrate the effectiveness of the UIO-based approach.

A wheelchair locomotion simulator (WCS) is an innovative solution to assess the biomechanical cost of wheelchairs (WC) accessibility in a controlled and safe virtual environment. In this context, this paper presents a haptic feedback control architecture based on a direct model reference adaptive control (MRAC) with intelligent tuning of its adaptation gains. The control objective is to follow the reference model velocity while producing the force feedback during the push phase, in order to faithfully recreate the dynamic behavior of the WC in a virtual environment. To accomplish this, a wheelchair ergometer model with friction is used to provide realistic navigation in the virtual environment (VE), by detecting and driving the wheelchair wheels. A two-wheeled vehicle model including the rolling resistance aspect is used to describe the wheelchair dynamic behavior. Since the controller adaptation gains are operated on the tracking error between the reference model and the simulator output, the WC model is also used as a reference model to specify the desired dynamics of the adaptive control system. For an optimal solution, an intelligent metaheuristic algorithm Elephant Herding Optimization (EHO) is employed to optimize the controller gain adaptation parameter to keep the tracking error as small as possible. Finally, the simulation results obtained show the effectiveness of the proposed control strategy.