Comprehensive Control Strategy Design for a Wheelchair Power-Assist Device

Rear add-ons are assistive devices developed to assist users who have difficulty propelling wheelchairs. Improving the mobility of wheelchair users and allowing them access to more activities is in line with the objective of the sustainable development goals SDG3, and SDG11. Currently, commercial rear add-on devices implement speed-based controls. The speed-based control consists in setting the reference speed that the device must keep constant which makes rear add-on devices suitable for long journeys but, makes them unsuitable for use in narrow spaces. In this paper an hybrid control is presented. The proposed control law takes into account the thrust exerted by the user (torque-based), the forward speed of the wheelchair (speed-based), as well as the surrounding environmental conditions. The total torque delivered by the device is evaluated as the sum of a contribution proportional to the user’s thrust, a delayed contribution as a function of forward speed, and the gravity compensation contribution. The proportional contribution synchronous with respect to the user’s push at low speeds improves manoeuvrability and controllability of the wheelchair, whereas, at higher speeds, the introduction of the delayed thrust distributes the assistance torque over a longer period, reducing the peak of torque provided by the device. A dynamic multibody model of a wheelchair was also developed and implemented in the Simulink environment to test the proposed control algorithm. As a future step the algorithm will be implemented on a rear add-on device and it will be tested experimentally by wheelchair users

Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications