This paper discusses the problem of squatting training of stroke patients. The main idea is to correct the patient's training trajectory through an iterative learning control (ILC) method. To obtain better rehabilitation effect, a patient will typically be required to practice a reference posture for many times, while most of active training methods can hardly keep the patients training with correct posture. Instead of the conventional ILC strategy, an impedance-based iterative learning method is proposed to regulate the impedance value dynamically and smartly which will help patients correct their posture gradually and perform better. To facilitate impedance-based ILC, we propose two objectives. The first objective is to find the suitable values of impedance based on the ILC scheme. The second objective is to search the moderate learning convergence speed and robustness in the iterative domain. The simulation and experimental results demonstrate that the performance of trajectory tracking will be improved greatly via the proposed algorithm.

A cornerstone of the rehabilitative regime for people diagnosed with dyskinesia is walking assist and gait training, however, prolonged care, provided by relatives or professionals, serves as a massive financial burden to patients. The proposed novel robotic walker seeks to address this concern. The walker incorporates human motion intention recognition to facilitate lower limb rehabilitation training and daily walking. The walker design places strong emphasis on patient safety and quality of life by providing omnidirectional walking assist, four key pelvic motions that support hip rotations and comfortable body weight support (BWS). Additionally, five sensors were installed to identify the user's motion intention from the interaction forces surrounding the pelvis and dead zone. Furthermore, Kalman filtering was used to guarantee the quality of the interactive signal while kinematic and dynamic models were derived to generate appropriate driving velocities to support patient's body weight and improve mobility. To validate our design, MATLAB simulations and exploratory clinical trials using healthy subjects were performed. Preliminary results demonstrate satisfactory kinematic performance and suggest the walker as a promising therapeutic avenue for individuals suffering from dyskinesia and other associated movement disorders.

In response to the ever-increasing demand of community-based rehabilitation, a novel smart rehab walker iReGo is designed to facilitate the lower limb rehabilitation training based on motion intention recognition. The proposed walker provides a number of passive degrees-of-freedom (DoFs) to the pelvis that are used to smooth the hip rotations in such a way that the natural gait is not significantly affected, meanwhile, three actuated DoFs are actively controlled to assist patients with mobility disabilities. The walker first identifies the user’s motion intention from the interaction forces in both left and right sides of the pelvis and then uses the kinematic model to generate appropriate driving velocities to support the body weight and improve mobility. In this paper, workspace, dexterity, and the force field of the walker are analyzed based on the system Jacobian. Simulation and experiments with healthy subjects are carried out to verify the effectiveness and tip-over stability. These results demonstrate that the walker has sufficient workspace for pelvic motions, satisfactory dexterity, and near-linear force feedback within the prescribed workspace, and that the walker is easily controlled to ensure normal gait.

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