For injured and after-stroke patients who temporarily lose their hand’s grasping abilities, assisting them in regaining their index finger mobility is very important in the rehabilitation process. In this paper, a finger rehabilitation device based on one degree-of-freedom (DOF) linkage mechanism is designed, aiming to lead the index finger through the flexion–extension trajectory during grasping tasks. Two types of one-DOF mechanisms, a four-bar linkage and a Watt-I six-bar linkage, are synthesized for the task trajectory. Various algorithms such as PSO, GA, and GA–BFGS are adopted and compared for the synthesis of these two types of mechanisms, among which the Watt-I six-bar linkage obtained with GA–BFGS shows the optimal performance in accuracy. Clinical biomechanical data are utilized to perform static analyses of the mechanisms, and the feasibility of the Watt-I six-bar linkage models is tested, compared, and demonstrated. Finally, the prototype of the six-bar linkage as well as a wearable exoskeleton finger rehabilitation device are designed to show how they are applied in the finger rehabilitation scenario.

Single degree-of-freedom (DOF) mechanisms have been widely used as motion executers for their compact structure and simple control. However, a single-DOF device can only lead through one specific motion trajectory. This article proposes an approach to design multimode single-DOF (MMSD) six-bar devices, which could realize multiple task trajectories using only one driving motor via an optimized structure with a selected adjustable parameter (A-Parameter). First, a source mechanism for multimode Watt-I six bar linkage is designed with nine candidate adjustable parameters, which enables the same mechanism to fit multiple different trajectories by selecting one specific parameter to be adjustable. Next, to determine the optimal A-Parameter, a multiobjective optimization algorithm nondominated sorting genetic algorithm II (NSGA-II)-adaptive rotation-based simulated binary crossover (ARSBX) is used to demonstrate and analyze the Pareto frontier optimization under different A-Parameters. Then, we take a family of gait trajectories from three different groups of volunteers as the task to illustrate the design of the MMSD six bar device. It shows that the optimal result appears when selecting $l_{1}$ as the A-parameter with the optimization window width $m=1$ . Finally, a prototype of the multimode source mechanism is built, and an application on the design of the MMSD gait rehabilitation device is configured and tested on the prototype, in which kinematic and dynamic analysis results both show a good realization of the tasks.

Single degree-of-freedom (DOF) mechanisms have been widely used as motion executers for their compact structure and simple control. However, a single-DOF mechanism can only lead through one specific motion trajectory. Thus, they usually cannot handle multiple tasks well, i.e., realizing a good fit of multiple trajectories. In order to solve such problems, this paper proposes an approach to design multi-mode single-DOF six bar mechanisms, which could individually realize multiple task trajectories using only one driving input via an optimized structure with a selected adjustable parameter (A-Parameter). First, a source mechanism for multi-mode watt-I six bar linkage is designed with 9 candidate adjustable parameters (link lengths, location of pivots, etc.), which enables the same mechanism to fit multiple trajectories by selecting only one parameter to be adjustable. Next, to select the optimal A-Parameter, a multi-objective optimization algorithm NSGA-II + ARSBX is used to demonstrate and analyze the Pareto front optimization under different A-Parameters. Then, we use an example of three different human gait trajectories to show how the synthesis multi-mode single-DOF six bar mechanism is conducted with our approach. In the end, the comparison of task trajectories and the resulting trajectories are presented, which show that the method proposed in this paper is feasible.

In order to help patients with lower-limb movement disorder to rehabilitate and restore suitable gaits, this paper seeks the design and optimization of one-DOF Stephenson-III six-bar mechanism for the realization of gait trajectories. A genetic optimization algorithm, GA-BFGS is used to obtain the dimensions of the six-bar mechanism so as to lead the end point to fit gait trajectories. In order to further understand the influence of different design parameters on the generated trajectory of the end point, sensitivity analysis has been conducted on nine design parameters (link lengths) based on the optimized dimension. In the end, nine sets of parameter sensitivity analysis results are presented and the trend of trajectory changes under different link lengths are analyzed, which could provide references for the manufacturing and assembly accuracy in actual rehabilitation device design.

Traditional trajectory planning approaches are currently lacking in intelligence and autonomy. We used the reinforcement learning approach to solve the autonomous trajectory planning of the robot arm to avoid obstacles with uniform motion and hit the target point quickly with obstacle avoidance planning for surgical robots taken as the practical background. We used the algorithm of experience playback mechanism combined with off-policy DDPG based on reinforcement learning, and after several iterations, the robot completed trajectory planning with obstacle avoidance autonomously. Moving obstacles were added to roughly simulate the autonomous obstacle avoidance of a surgical robotic arm with moving medical personnel or mobile instruments in the operating room, based on the simple trajectory planning example of Open-AI Open-Source Project Baseline, combined with the research context. Sparse rewards were used for each iteration based on the HER algorithm, so that each attempt could gain experience. The HER-DDPG method can quickly complete the manipulator's trajectory planning in a simulation environment, which is critical for the surgical robot's autonomous positioning in the real world. Furthermore, the experience playback system has been tested to allow full use of sparse rewards and handle parallel tasks equally well.

Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern; hence, we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. By using the regression motion of the clustering result as the target, in this article, we seek to apply kinematic mapping-based motion synthesis framework to design a 1-degree-of-freedom (DOF) mechanism, such that it could lead the patients' upper limb through the target motion. Also, considering rehab training generally involves a large amount of repetition on a daily basis, this article has developed a rehab system with unity3d based on virtual reality (VR). The proposed device and system could provide an immersive experience to the users, as well as the rehab motion data to the administrative staff for evaluation of users' status. The construction of the integrated system and the experimental trial of the prototype are presented at the end of this article.

A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower limb rehab patients is proposed to provide the basis for the design of simple 1-degree-of-freedom (DOF) rehab mechanisms. First, a large amount of gait trajectories from various healthy volunteers are collected along with their body parameters, and a normalization method is presented to trim/expand these trajectory samples to a standard length and timing while retaining their shape and velocity information. Then, these normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithm-optimized support vector machine method is adopted to train a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, it can predict a most suitable gait trajectory for the specific patient. It shows that the accuracy of trajectory prediction reaches 96% both on training set and test set which verifies the effectiveness of the method. In the end, a 1-DOF gait rehab mechanism design example is provided to illustrate the application of the proposed method. Taking the predicted result from the classifier as the task motion trajectory for the synthesis of mechanisms, a 1-DOF six-bar mechanism is designed and the patient-mechanism matching can be realized.

Mechanical devices such as robots are widely adopted for limb rehabilitation. Due to the variety of human body parameters, the rehabilitation motion for different patients usually has its individual pattern, thus we adopt clustering-based machine learning technique to find a limited number of motion patterns for upper-limb rehabilitation, so that they could represent the large amount of those from people who have various body parameters. Using the regression motion of the clustering result as the task motion, in this paper we seek to apply kinematic-mapping-based motion synthesis framework to design a one-DOF mechanism such that it could lead the patients’ upper limb through the task motion. Also,considering rehab training generally involves a large amount of repetition in daily basis, this paper has developed an immersive rehab system with Unity3D based on Virtual Reality (VR). A patient user interface as well as an administrator user interface are presented, and a two-mode rehabilitation strategy is proposed. The construction of the integrated system and a prototype of the upper limb rehab device are also shown in the end of this paper.