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BackgroundFew options exist for training arm movements in participants with severe post-stroke hemiparesis who have little active range of motion. The purpose of this study was to test the safety and feasibility of training with a non-powered device, the Bimanual Arm Trainer (BAT), to facilitate motor recovery in individuals with severe hemiparesis. The BAT enabled coupled bimanual training of shoulder external rotation, which is reduced in individuals with severe post-stroke hemiplegia. The rationale for bimanual training was to harness contralesional cortical activity to drive voluntary movement in the affected arm in patients who could barely perform unimanual movements.MethodsNine participants with post-stroke hemiparesis, preserved passive range of motion and Modified Ashworth score of <3 in the shoulder and elbow joints, trained with the device for 45 minutes, twice a week for six weeks, and were assessed pre- and post-training.ResultsAll participants tolerated the training and no adverse events were reported. Participants showed significant improvement in the upper extremity Fugl-Meyer score post-training with an effect size of 0.89. Changes in the flexor synergy pattern accounted for 64.7% of the improvement. Improvement in active range of motion in the paretic limb occurred for both trained and untrained movements. Some participants showed improvement in the time taken to perform selected tasks on the Wolf Motor Function Test post-training.ConclusionThe results demonstrate the safety and feasibility of using the Bimanual Arm Trainer to facilitate motor recovery in individuals with severe hemiparesis.

Articulated heavy vehicles have some specific performance limitations and safety risks due to their special dynamic characteristics. They show poor manoeuvrability at low speeds and may lose their stability in different manners at high speeds. In this study, the potential of active steering control of the semitrailer on manoeuvrability and stability of tractor-semitrailer combinations is investigated. A linear bicycle model and a nonlinear version are used for controller design and vehicle dynamic simulation in MATLAB environment. The Linear Quadratic Regulator optimal state feedback control is used to minimise the tracking error at low-speed, and regulate Rearward Amplification ratio and roll at high-speed. Quantum Particle Swarm Optimisation is used for optimising the weighting factors. Three different control algorithms are introduced and it is demonstrated through simulations that the vehicle with the proposed steering control exhibits desirable improvements compared to the baseline vehicle.

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