Robot-assisted task-oriented training demonstrates immense potential in rehabilitation area. Parallel robots, with advantages such as low inertia and high stiffness, facilitate precise haptic feedback, yet their application in rehabilitation is limited by workspace constraints. To this end, we propose a design scheme for a haptic robot based on a reconfigurable asymmetric parallel mechanism. We first introduce a two-stage multi-objective optimization method to obtain the optimal parameter configurations. Then, to achieve precise assembling of the reconfigurable mechanism in each configuration, corresponding positioning mechanisms are designed. System performance tests validate the robot's capabilities under different configurations: workspace meets design requirements, stiffness output reaches 30 N/mm, force output is 40 N, RMS of maximum back-driven force along$x, y$, and$z$axes is 7.5 N, and RMS of maximum back-driven torque around$x$and y axes is 567.4 N. mm. Target tracking and virtual channel trajectory tracking experiments demonstrate the system's haptic rendering ability for gross motor tasks (GMTs) and fine motor tasks (FMTs), respectively. The developed 6-DOF haptic robot holds promise for versatile task-oriented rehabilitation training.

Emerging evidence suggests that prolonged virtual reality (VR) exposure may impair human sensory systems. Most research has focused on the visual, proprioceptive, and vestibular systems, but the impact of VR on haptic perception remains unclear. In this study, we investigated alterations in human sensitivity to contact force following VR gaming. A force perception task was designed to assess changes in contact force across six difficulty levels with step sizes ranging from 0.5 to 5 N. A total of 18 participants performed the task before VR, after 10 min, and after an additional 20 min of VR. The perceptual accuracy of correctly perceiving force changes at each difficulty level was measured across three test periods. The results indicated that 66.67% of participants experienced a negative impact from VR at the 1-N change step. Perceptual accuracy significantly decreased in this group, with a 9.17% reduction after 10 min and a 17.50% reduction after an additional 20 min. In contrast, minimal effects were observed in the remaining participants. These findings suggest that even short-term VR exposure can impair force discrimination in certain users, with the effects becoming more pronounced over time.

Restoring the capability to perform instrumental activities of daily living (IADLs) is an imperative step towards independent living for neurologically impaired individuals. Robot-assisted task-oriented training with haptic feedback has the potential to enhance patients’ ability to perform IADLs. However, robot-assisted haptic rendering of IADLs is extremely challenging due to their complex dynamic properties and has been rarely reported. Considering the broad impedance range characteristics from free motion to hard contact, nail hammering (NH) is chosen as a representative IADL task. This paper presents our attempts to render the NH task via a customized robot. The core technologies consist of two aspects: 1) a robot-assisted haptic modeling technique with guaranteed accuracy and computation cost (by combining practical measurement data and experience-dependent analytical functions); 2) a robot-assisted haptic rendering technique involving a haptic robot with broad impedance range and sufficient force feedback (via a low gear ratio cable transmission and redundant actuation parallel mechanism) and closed-loop impedance control with guaranteed passivity and stability. Human experiments demonstrate an accurate NH task rendering that all Pearson correlation coefficients between real and virtual tasks are larger than 0.89. The modeling sensitivity analysis showed that stiffness parameter has the greatest effect on the realism of haptic rendering, with an effect size of 0.94. This study represents an important step towards comprehensive robot-assisted task-oriented therapy with haptic feedback.Note to Practitioners—The motivation of this work is to explore the techniques of robot-assisted haptic rendering of IADL tasks. On the one hand, current task modeling approaches are hard to balance accuracy and computational efficiency. On the other hand, existing haptic platforms have difficulty in meeting the broad impedance range and large force output requirements. In this work, we firstly developed a customized haptic robot via redundant actuation (enabling high robotic stiffness and force output) and low gear ratio cable transmission (enabling low friction and high back-drivability). We then built the nail hammering (NH) task model by combining practical measurement data (for accuracy) and experience-dependent analytical functions (for computational efficiency). Finally, we achieved the haptic rendering of the NH task using closed-loop impedance control with passivity and stability analysis. The proposed robot-assisted haptic modeling and rendering techniques can be extended to the haptic display of other types of IADL tasks.

Task-oriented training with haptic rendering can boost robot-aided motor learning to tasks with similar dynamics. Although multi-DOF robots better match the rendering of real task scenarios, single-DOF haptic robots show great potential for home use with enhanced task rendering performance. This study presents our attempts to optimize and develop a single-DOF cable-driven robot with appropriate workspace and force rendering capacity. The core technologies consist of two aspects: 1) a multi-objective optimization method was adopted to obtain optimal configuration of the haptic robot; and 2) a slider-crank-mechanism-based portable cable-driven robot was developed. Performance evaluation experiments demonstrated that 1) the robot has a workspace larger than 300 mm; 2) the robot can achieve 40 N force output and 40 N. mm-1stiffness for hard contact; 3) the root mean square of the resistance during free motion is 0.93 N; 4) in the purely passive case (without motor compensation), the average resistance to back drive the motor is 2.5 N. These lead us to believe that the developed robot holds the promise to serve as a robotic rehabilitation training platform for home use on the neurological-impaired patients.

Parallel mechanisms (PMs) are commonly used for developing haptic devices due to low inertia, high rigidity and precision. However, limited workspace impedes their application for task-oriented robotic therapy which generally requires large motion ranges. To solve this problem, first, a PM- based reconfigurable asymmetric 6-DOF haptic interface was presented, and then a two-stage optimization method was proposed to make the robot implement two kinds of task-specific workspaces including gross motor tasks (GMTs) and fine motor tasks (FMTs). Optimization of this robot was conducted to pursue a compact size and high accuracy. The global conditioning index (GCI) and the occupied area of the robot were selected as the evaluation indices, where the GCI was derived using a dimensionally homogeneous Jacobian matrix. A multi-objective optimization method based on the genetic algorithm (GA) was utilized. The actual design parameters were finally defined from solutions of the Pareto front. The proposed two-stage optimization method provides a feasible solution for determining task-specific robotic workspace of the reconfigurable mechanism.