Virtual Reality (VR) training has gained popularity in
recent years due to its versatility and safety in applications
such as industrial education and rehabilitation. The addition
of haptic information [1] during VR training, e.g., on the
physical properties of a virtual object like mass and inertial
forces, has been shown to enhance motor learning [2] and
increase movement economy and precision [3]. However,
rendering these dynamic forces remains a challenge, par-
ticularly for ungrounded haptic devices. While ungrounded
devices allow for a large free workspace, they often face
limitations such as high cost, latency, and side effects through
noise, vibrations, or airflow [4]. To address these limitations,
we present the first design and evaluation of LeVR, a low-
cost, portable haptic proxy (see Fig. 1). LeVR aims to provide
information about virtual objects’ weight by rendering the
vertical forces experienced when lifting objects. It achieves
this by dynamically accelerating a motorized sled along a
linear rail upon interaction with the virtual object, allowing
users to perceive differences in object weight through a
simple and portable design.

Neuroscience evidence suggests that personalized, task-specific, high-intensity training is essential for maximizing recovery after acquired brain injury. Robotic devices combined with immersive virtual reality (VR) games, visualized through head-mounted displays (HMDs), can support such intensive training within naturalistic virtual environments with audio-visual stimuli tailored to individual needs. However, the impact of these auditory and visual demands on cognitive load remains an open question. To address this, we conducted an experiment with 22 healthy participants to explore how varying levels of visual, auditory, and cognitive demands affect users’ cognitive load and performance during a shopping task in immersive VR. We found that mental demand had the most significant impact on increasing cognitive load and hampering task performance. Visual demands, although affecting gaze behavior, did not significantly affect cognitive load or performance. Auditory demands showed small effects on cognitive load.

Robotic rehabilitation systems may benefit from haptic rendering to provide sensorimotor training to patients with acquired brain injuries. Haptic rendering usually involves modulating stiffness and viscosity to simulate real-world hand-object interactions. Yet, the effect of rendering different viscosities on brain activity remains mainly unexplored. To fill this gap, we ran an experiment with twelve unimpaired participants who were asked to grasp and release virtual liquid dispensers whose stiffness and viscosity were rendered using a haptic hand rehabilitation robot. All liquid dispensers had identical wall stiffness but contained liquids of three different viscosities. We also incorporated control conditions without viscosity and stiffness rendering, involving both passive and active grasping movements. Electroencephalography data were recorded during the experiment. We found stronger ipsilateral somatosensory mu and beta event-related desynchronization during movements with viscosity and stiffness rendering compared to the control conditions, while different viscosity levels did not result in significant variations. Furthermore, no significant electroencephalography activity differences were found between control conditions. These findings indicate that while viscosity and stiffness rendering strengthens brain activity, modulating viscosity levels does not significantly affect this response. This insight may contribute to the design of rehabilitation games by informing the choice of viscosity rendering parameters.

BACKGROUND: Head-mounted displays can be used to offer personalized immersive virtual reality (IVR) training for patients who have suffered an Acquired Brain Injury (ABI) by tailoring the complexity of visual and auditory stimuli to the patient's cognitive capabilities. However, it is still an open question how these virtual environments should be designed. METHODS: We used a human-centered design approach to help define the characteristics of suitable virtual training environments for ABI patients. We conducted (i) observations, (ii) interviews with eleven neurorehabilitation experts, and (iii) an online questionnaire with 24 neurorehabilitation experts to examine how therapists modify current training environments to promote patients' recovery in conventional sensorimotor neurorehabilitation settings. Finally, (iv) we involved eight neurorehabilitation experts in a participatory design workshop to co-create examples of IVR training environments. RESULTS: Five phases of the recovery process (Screening, Planning, Training, Reflecting, and Discharging) and six key themes describing the characteristics of suitable (physical) training environments (Specific, Meaningful, Versatile, Educational, Safe, and Supportive) were identified. The experts agreed that modulating the number of elements (e.g., objects, people) or distractions (e.g., background noise) in the physical training environment enables therapists to provide their patients with suitable conditions to execute functional tasks. Additionally, the experts highlighted the importance of developing IVR training environments that are meaningful and realistic. CONCLUSIONS: Through consultations with neurorehabilitation experts, we gained insights into how therapists adjust physical training environments to promote the execution of functional sensorimotor tasks in patients with diverse cognitive capabilities. Their recommendations on how to modulate and make IVR environments meaningful may contribute to increased motivation and skill transfer. Future studies on IVR-based neurorehabilitation should involve patients themselves.

Motor augmentation (MA) is an emerging field at the intersection of engineering, robotics, and neuroscience, aiming to enhance human capabilities through the integration of extra limbs. This concept leverages the body’s physiological redundancies, including those within the nervous system. This study examined motor imagery (MI) involving a virtual extra arm, focusing on differentiating its neural patterns from those of biological limbs. Thirty participants performed unimanual reaching MI tasks before (Pre) and after (Post) a conditioning phase in a virtual environment, during which half of the participants received tactile feedback on the movement of the extra arm. Electroencephalographic (EEG) recordings revealed distinct event-related desynchronization (ERD) in α and β rhythms between the extra and biological limbs. Additionally, a Riemannian decoder successfully classified MI for the left, right, and extra virtual arm, providing further evidence of distinct neural patterns. While the conditioning played a role in the ERD’s neural signatures, we did not find the same effects on the decoding. We believe that more complex movements, other sensory encoding modalities, or longer conditioning periods would likely strengthen the connection between tactile feedback and neural control.

Gait variability, the subtle fluctuations in walking patterns, is crucial for adaptation and motor learning. While existing methods to increase gait variability often rely on force-based perturbations, these can reduce motivation. This study explored if a subtle visual feedback distortion (VFD), applied to a first-person avatar's foot position in an immersive virtual reality environment, could increase gait variability without such a drawback. Twenty healthy adults walked on a treadmill wearing a head-mounted display and motion trackers, performing a stepping task under two conditions: with and without VFD. The VFD introduced a continuously changing, noise-like offset to the displayed foot positions, designed to be minimally noticeable. We quantified gait variability through the standard deviation of step width and step length and collected self-report measures on embodiment, motivation, and simulator sickness. We found that VFD significantly increased step width variability by about 15%, indicating enhanced lateral adaptability. In contrast, step length variability remained unchanged. Participants adjusted their foot placement in the opposite direction of the visual distortion, supporting the idea that proprioceptive recalibration underpinned the observed changes. Notably, this increase in variability occurred without any significant effects on embodiment, motivation, or simulator sickness. These findings suggest that subtle VFD can enhance gait variability - potentially facilitating motor learning and adaptability - while preserving user experience and motivation. Future research should determine whether such VFD-based interventions yield lasting functional improvements and investigate their applicability in rehabilitation contexts, potentially offering a noninvasive, user-friendly approach to promoting healthy gait dynamics.

Background: Robotic devices have shown promise in supporting motor (re)learning. However, there is a limited understanding of how personality traits influence the effectiveness of robot-aided training strategies. Methods: We conducted a motor learning experiment with 40 unimpaired participants who trained to control a virtual pendulum using a robotic haptic device. Before the experiment, we assessed personality traits including the perceived control over life events (Locus of Control), the tendency to turn challenges into engaging activities (Transform of Challenge), and other subscales from Autotelic and Hexad gaming style questionnaires. Participants were divided into two groups, one receiving haptic guidance during training and a second one without assistance. Short- and long-term retention was assessed, and relationships between personality traits, performance metrics, and human-robot interaction metrics were analyzed. Results: Participants with high Transform of Challenge or external Locus of Control characteristics who received physical guidance during training reduced the human-robot interaction forces to a lesser extent compared to the ones who did not receive guidance. Additionally, participants with a high Free Spirit gaming style showed greater sensitivity to how their perception of the guidance affected their performance during the retention phases. Conclusion: Our findings suggest that autotelic personality, Locus of Control, and gaming style modulate motor learning outcomes during robotic-assisted training, affecting both performance and human-robot interaction metrics. This highlights the potential of integrating personality-based adaptations in robot-aided rehabilitation protocols to enhance performance and motor (re)learning. Future works should explore the relationship between personality traits and psychological states (e.g., perceived difficulty, attention) across diverse tasks and guidance methods in clinical populations.

Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effector add-on for treadmills that couples the movement of the paretic and non-paretic legs via a reciprocating system of elastic cables and pulleys. Two human-device mechanical interfaces were designed to attach the elastic cables to the user. The P-MATE and two interface prototypes were tested with a physical therapist and eight unimpaired participants. Biomechanical data, including kinematics and interaction forces, were collected alongside standardized questionnaires to assess usability and user experience. Both interfaces were quick and easy to attach, though user experience differed, highlighting the need for personalization. We also identified areas for future improvement, including pretension adjustments, tendon derailing prevention, and understanding long-term impacts on user gait. Our preliminary findings underline the potential of the P-MATE to provide effective, accessible, and sustainable stroke gait rehabilitation.

Robotic devices, in combination with virtual reality games, have the potential to increase therapy dosage while enhancing patient’s motivation. Yet, current robotic interventions suffer from poor usability, over-reliance on the availability of trained therapists, and the inability to provide meaningful somatosensory information despite its importance for relearning skillful movements. To address this gap, we co-created two novel haptic rehabilitation robots for in-clinic and in-home rehabilitation capable of high-fidelity haptic rendering during functional reach and grasp training in motivating virtual games together with rehabilitation experts. We evaluated the usability of our solutions with therapists and patients following a mixed-methods approach, gathering quantitative and qualitative data from questionnaires and semi-structured interviews. The results showed good usability and high enjoyment, with the fidelity of virtual object interactions highly praised. Some mechanical design improvements, mainly with regard to comfort, were also identified. Our devices offer naturalistic sensations during training, paving the way for more holistic sensorimotor neurorehabilitation.

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.

INTRODUCTION: There is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering. METHODS: We developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients' hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution. RESULTS: The device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system's available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this. CONCLUSION: The novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation.

To design effective rehabilitative technology, stakeholders (e.g., professionals from hospitals, universities, and industries) must empathize with end-user experiences and actively involve them throughout the design process. This approach can ensure the understanding of their complex needs. Yet end-user involvement is often limited to testing only. Technology developers often underestimate the valuable insights end-users gain during their recovery, which extend beyond technical knowledge. To address this, our international team of designers, engineers, and clinical personnel proposes a participatory design workshop involving acquired brain injury patients and their caregivers. Patients and caregivers work in groups with workshop participants to address specific needs and use methods like personas, MoSCoW prioritization, and prototyping to co-create solutions to meet those needs. We aim to illustrate the benefits of this approach and encourage participants to adopt participatory design in their future developments.

The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning outcomes and cater better to the trainee's needs and desires. However, the development of personalized haptic assistance is hindered by the lack of understanding of the link between the trainee's personality and the effects of haptic guidance during human-robot interaction. To address this gap, we ran an experiment with 40 healthy participants who trained with a robotic device to control a virtual pendulum to hit incoming targets, either with or without haptic guidance. We found that certain personal traits affected how users adapt and interact with the guidance during training. In particular, those participants with an 'Achiever gaming style' performed better and applied lower interaction forces to the robotic device than the average participant as the training progressed. Conversely, participants with the 'Free spirit game style' increased the interaction force in the course of training. We also found an interaction between some personal characteristics and haptic guidance. Specifically, participants with a higher 'Transformation of challenge' trait exhibited poorer performance during training while receiving haptic guidance compared to an average participant receiving haptic guidance. Furthermore, individuals with an external Locus of Control tended to increase their interaction force with the device, deviating from the pattern observed in an average participant under the same guidance. These findings suggest that individual characteristics may play a crucial role in the effectiveness of haptic guidance training strategies.

Purpose: Virtual Reality (VR) has proven to be an effective tool for motor (re)learning. Furthermore, with the current commercialization of low-cost head-mounted displays (HMDs), immersive virtual reality (IVR) has become a viable rehabilitation tool. Nonetheless, it is still an open question how immersive virtual environments should be designed to enhance motor learning, especially to support the learning of complex motor tasks. An example of such a complex task is triggering steps while wearing lower-limb exoskeletons as it requires the learning of several sub-tasks, e.g., shifting the weight from one leg to the other, keeping the trunk upright, and initiating steps. This study aims to find the necessary elements in VR to promote motor learning of complex virtual gait tasks. Methods: In this study, we developed an HMD-IVR-based system for training to control wearable lower-limb exoskeletons for people with sensorimotor disorders. The system simulates a virtual walking task of an avatar resembling the sub-tasks needed to trigger steps with an exoskeleton. We ran an experiment with forty healthy participants to investigate the effects of first- (1PP) vs. third-person perspective (3PP) and the provision (or not) of concurrent visual feedback of participants’ movements on the walking performance – namely number of steps, trunk inclination, and stride length –, as well as the effects on embodiment, usability, cybersickness, and perceived workload. Results: We found that all participants learned to execute the virtual walking task. However, no clear interaction of perspective and visual feedback improved the learning of all sub-tasks concurrently. Instead, the key seems to lie in selecting the appropriate perspective and visual feedback for each sub-task. Notably, participants embodied the avatar across all training modalities with low cybersickness levels. Still, participants’ cognitive load remained high, leading to marginally acceptable usability scores. Conclusions: Our findings suggest that to maximize learning, users should train sub-tasks sequentially using the most suitable combination of person’s perspective and visual feedback for each sub-task.

Head-mounted displays can offer personalized immersive virtual reality (VR) training for patients who have suffered an Acquired Brain Injury by tailoring the complexity of visual and auditory stimuli to their cognitive capabilities. However, how these virtual environments should be designed remains undetermined. We conducted a participatory design workshop with eight neurorehabilitation experts to collect their opinions on using immersive VR-based neurorehabilitation and co-create examples of low and high-cognitively demanding immersive virtual training environments. Participants highlighted the importance of developing meaningful and realistic environments. This study provides an example of a high-tech co-creation workshop whose results provide insights into designing training environments in immersive VR to meet patients’ needs.

We evaluated the impact of Model Predictive Control (MPC) robotic-assisted versus unassisted training on motor learning of a complex bicycle steering task. Ten participants were divided into two groups, alternating between MPC-assisted and unassisted training to ride a steer-by-wire bicycle on a treadmill to collect virtual stars. At Baseline, Mid-Training, and Post-Training, motor skills were assessed by the average and standard deviation (SD) of distance to stars, while performance was measured by the mean absolute and SD of the steering rate. We found significant improvements in task skill and steering performance, with notable benefits observed in the performance of the group initially trained unassisted. Our findings suggest that starting the training unassisted could stimulate an internal focus (concentrating on one's own body movements) and intrinsic skill perception. This foundation may then form a basis for later integration of MPC assistance to refine further the gained motor skills. Such a sequential training approach may benefit motor skill acquisition of complex dynamics tasks. Further research is necessary to validate and apply these findings to enhance training methods.

A large proportion of stroke survivors suffer from sensory loss, negatively impacting their independence, quality of life, and neurorehabilitation prognosis. Despite the high prevalence of somatosensory impairments, our understanding of somatosensory interventions such as sensory electrical stimulation (SES) in neurorehabilitation is limited. We aimed to study the effectiveness of SES combined with a sensory discrimination task in a well-controlled virtual environment in healthy participants, setting a foundation for its potential application in stroke rehabilitation. We employed electroencephalography (EEG) to gain a better understanding of the underlying neural mechanisms and dynamics associated with sensory training and SES. We conducted a single-session experiment with 26 healthy participants who explored a set of three visually identical virtual textures—haptically rendered by a robotic device and that differed in their spatial period—while physically guided by the robot to identify the odd texture. The experiment consisted of three phases: pre-intervention, intervention, and post-intervention. Half the participants received subthreshold whole-hand SES during the intervention, while the other half received sham stimulation. We evaluated changes in task performance—assessed by the probability of correct responses—before and after intervention and between groups. We also evaluated differences in the exploration behavior, e.g., scanning speed. EEG was employed to examine the effects of the intervention on brain activity, particularly in the alpha frequency band (8–13 Hz) associated with sensory processing. We found that participants in the SES group improved their task performance after intervention and their scanning speed during and after intervention, while the sham group did not improve their task performance. However, the differences in task performance improvements between groups only approached significance. Furthermore, we found that alpha power was sensitive to the effects of SES; participants in the stimulation group exhibited enhanced brain signals associated with improved touch sensitivity likely due to the effects of SES on the central nervous system, while the increase in alpha power for the sham group was less pronounced. Our findings suggest that SES enhances texture discrimination after training and has a positive effect on sensory-related brain areas. Further research involving brain-injured patients is needed to confirm the potential benefit of our solution in neurorehabilitation.

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.

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.

Research on motor learning has found evidence that learning rate is positively correlated with the learner's motor variability. However, it is still unclear how to robotically promote that variability without compromising the learner's sense of agency and motivation, which are crucial for motor learning. We propose a novel method to enhance motor variability during learning of a dynamic task by applying pseudo-random perturbing forces to the internal degree of freedom of the dynamic system rather than directly applying the forces to the learner's limb. Twenty healthy participants practiced swinging a virtual pendulum to hit oncoming targets, either with the novel method or without disturbances, to evaluate the effect of the method on motor learning, skill transfer, motivation, and agency. We evaluated skill transfer using two tasks, changing either the target locations or the task dynamics by shortening the pendulum rod. The indirect haptic disturbance method successfully increased participants' motor variability during training compared to training without disturbance. Although we did not observe group-level differences in learning, we observed divergent effects on skill generalization. The indirect haptic disturbances seemed to promote skill transfer to the altered task dynamics but limited transfer in the task with altered target positions. Motivation was not affected by the haptic disturbances, but future work is needed to determine if indirect haptic noise negatively impacts sense of agency. Increasing motor variability by indirect haptic disturbance is promising for enhancing skill transfer in tasks that incorporate complex dynamics. However, more research is needed to make indirect haptic disturbance a valuable tool for real-life motor learning situations.