In current virtual reality settings for motor skill training, only visual information is usually provided regarding the virtual objects the trainee interacts with. However, information gathered through cutaneous (tactile feedback) and muscle mechanoreceptors (kinesthetic feedback) regarding, e.g., object shape, is crucial to successfully interact with those objects. To provide this essential information, previous haptic interfaces have targeted to render either tactile or kinesthetic feedback while the effectiveness of multimodal tactile and kinesthetic feedback on the perception of the characteristics of virtual objects still remains largely unexplored. Here, we present the results from an experiment we conducted with sixteen participants to evaluate the effectiveness of multimodal tactile and kinesthetic feedback on shape perception. Using a within-subject design, participants were asked to reproduce virtual shapes after exploring them without visual feedback and with either congruent tactile and kinesthetic feedback or with only kinesthetic feedback. Tactile feedback was provided with a cable-driven platform mounted on the fingertip, while kinesthetic feedback was provided using a haptic glove. To measure the participants’ ability to perceive and reproduce the rendered shapes, we measured the time participants spent exploring and reproducing the shapes and the error between the rendered and reproduced shapes after exploration. Furthermore, we assessed the participants’ workload and motivation using well-established questionnaires. We found that concurrent tactile and kinesthetic feedback during shape exploration resulted in lower reproduction errors and longer reproduction times. The longer reproduction times for the combined condition may indicate that participants could learn the shapes better and, thus, were more careful when reproducing them. We did not find differences between conditions in the time spent exploring the shapes or the participants’ workload and motivation. The lack of differences in workload between conditions could be attributed to the reported minimal-to-intermediate workload levels, suggesting that there was little room to further reduce the workload. Our work highlights the potential advantages of multimodal congruent tactile and kinesthetic feedback when interacting with tangible virtual objects with applications in virtual simulators for hands-on training applications.@en

In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that incorporates active grasping movements and passive pronosupination, complemented by a rehabilitative game with meaningful haptic feedback. Following a human-centered design approach, we conducted a usability study with 13 healthy participants, including three therapists. In a simulated unsupervised environment, the naive participants had to set up and use the device based on written instructions. Our mixed-methods approach included quantitative data from performance metrics, standardized questionnaires, and eye tracking, alongside qualitative feedback from semi-structured interviews. The study results highlighted the device's overall ease of setup and use, as well as its realistic haptic feedback. The eye-tracking analysis further suggested that participants felt safe during usage. Moreover, the study provided crucial insights for future improvements such as a more intuitive and comfortable wrist fixation, more natural pronosupination movements, and easier-to-follow instructions. Our research underscores the importance of continuous testing in the development process and offers significant contributions to the design of user-friendly, unsupervised neurorehabilitation technologies to improve sensorimotor stroke rehabilitation.@en

Haptic rendering of weight plays an essential role in naturalistic object interaction in virtual environments. While kinesthetic devices have traditionally been used for this aim by applying forces on the limbs, tactile interfaces acting on the skin have recently offered potential solutions to enhance or substitute kinesthetic ones. Here, we aim to provide an in-depth overview and comparison of existing tactile weight rendering approaches. We categorized these approaches based on their type of stimulation into asymmetric vibration and skin stretch, further divided according to the working mechanism of the devices. Then, we compared these approaches using various criteria, including physical, mechanical, and perceptual characteristics of the reported devices. We found that asymmetric vibration devices have the smallest form factor, while skin stretch devices relying on the motion of flat surfaces, belts, or tactors present numerous mechanical and perceptual advantages for scenarios requiring more accurate weight rendering. Finally, we discussed the selection of the proposed categorization of devices together with the limitations and opportunities for future research. We hope this study guides the development and use of tactile interfaces to achieve a more naturalistic object interaction and manipulation in virtual environments.

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Object properties perceived through the tactile sense, such as weight, friction, and slip, greatly influence motor control during manipulation tasks. However, the provision of tactile information during robotic training in neurorehabilitation has not been well explored. Therefore, we designed and evaluated a tactile interface based on a two-degrees-of-freedom moving platform mounted on a hand rehabilitation robot that provides skin stretch at four fingertips, from the index through the little finger. To accurately control the rendered forces, we included a custom magnetic-based force sensor to control the tactile interface in a closed loop. The technical evaluation showed that our custom force sensor achieved measurable shear forces of ± 8 N with accuracies of 95.2-98.4 % influenced by hysteresis, viscoelastic creep, and torsional deformation. The tactile interface accurately rendered forces with a step response steady-state accuracy of 97.5-99.4% and a frequency response in the range of most activities of daily living. Our sensor showed the highest measurement-range-to-size ratio and comparable accuracy to sensors of its kind. These characteristics enabled the closed-loop force control of the tactile interface for precise rendering of multi-finger two-dimensional skin stretch. The proposed system is a first step towards more realistic and rich haptic feedback during robotic sensorimotor rehabilitation, potentially improving therapy outcomes.

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Rehabilitation robotics combined with virtual reality using head-mounted displays enable naturalistic, immersive, and motivating therapy for people after stroke. There is growing interest in employing digital twins in robotic neurore-habilitation, e.g., in telerehabilitation for virtual coaching and monitoring, as well as in immersive virtual reality applications. However, the kinematic matching of the robot's visualization with the real robot movements is hardly validated, potentially affecting the users' experience while immersed in the virtual environment due to a visual-proprioceptive mismatch. The kinematic mismatch may also limit the validity of assessment measures recorded with the digital twin. We present the development and low-cost kinematic validation of a digital twin of a seven active degrees-of-freedom exoskeleton for stroke rehabilitation. We validated the kinematic accuracy of the digital twin end-effector by performing two tasks-a planar reaching task and a 3D functional task-performed by a single healthy participant. We computed the end-effector position and rotation from the forward kinematics of the robot, the digital twin, and data recorded from the real robot using a low-cost tracking system based on HTC VIVE trackers and compared them pair-wise. We found that the digital twin closely matches the forward kinematics and tracked movement of the real robot and thus provides a reliable platform for future research on digital twins for stroke rehabilitation.@en

High transparency is a fundamental requirement for upper-limb exoskeletons to promote active patient participation. Although various control strategies have been suggested to improve the transparency of these robots, there are still some limitations, such as the need for precise dynamic models and potential safety issues when external forces are applied to the robot. This study presents a novel hybrid controller designed to tackle these limitations by combining a traditional zero-torque controller with an interaction torque observer that compensates for residual undesired disturbances. The transparency of the proposed controller was evaluated using both quantitative-e.g., residual joint torques and movement smoothness-and qualitative measures-e.g., comfort, agency, and perceived resistance-in a pilot study with six healthy participants. The performance of the new controller was compared to that of two conventional controllers: a zero-torque closed-loop controller and a velocity-based disturbance observer. Our preliminary results show that the proposed hybrid controller may be a good alternative to state-of-the-art controllers as it allows participants to perform precise and smooth movements with low interaction joint torques. Importantly, participants rated the new controller higher in comfort and agency, and lower in perceived resistance. This study highlights the importance of incorporating both quantitative and qualitative assessments in evaluating control strategies developed to enhance the transparency of rehabilitation robots.@en