Task-Dependent Adaptations in Closed-Loop Motor Control Based on Electrotactile Feedback

Tactile feedback plays an important role in hand manipulation, especially in the grasping process which is one of the major functions of the hand. However, few commercially available prosthetic hands or hand motor function rehabilitation systems are equipped with tactile feedback. The absence of suitable tactile feedback modules leads to an inferior rehabilitation performance with a large burden on user training and compromised usability. Thus, it is challenging and essential to integrate a proper tactile feedback module with the existing hand rehabilitation systems to achieve a better control performance and accelerate the rehabilitation process. This paper focuses on the implementation and evaluation of the electrotactile feedback (EF) enhanced rehabilitation system. A virtual hand rehabilitation platform is proposed comprising an surface electromyography (sEMG) acquisition module, an electrotactile stimulation module, a virtual environment with sEMG-driven humanlike hand and numerical feedbacks of grasping force and fingertip deformation, where a closed-loop control is formed. Three different feedback conditions including visual feedback (VF), EF, and no feedback (NF) are compared based on the proposed platform. Experiments were conducted on 10 able-bodied subjects, and multiple quantitative metrics for the rehabilitation performance evaluation including training burden estimation and success rate (SR) of tasks were adopted. Results indicate that the integration of EF is helpful to both reduce the rehabilitation duration and improve the virtual grasping SR in comparison with the NF condition while possessing a better practicality over VF. Note to Practitioners —This paper is motivated by the problem of hand grasp control for rehabilitation purposes, but it also applies to other hand motor function rehabilitation process. Existing hand motor function rehabilitation approaches generally lack a proper feedback and rely on the heavy burden during user training and the users experience. This paper suggests incorporating EF to improve the efficiency and efficacy of the rehabilitation process. The electrical stimulation is driven by the myoelectric-sensing-based force estimation and encoded in a manual scheme to fit each individual involved. In our work, a virtual hand rehabilitation platform is implemented to verify the feasibility of EF in reducing the burden of user training and improving the rehabilitation performance, which allows the expanding of the current system into a broader spectrum of motor function rehabilitation applications. Experiments on able-bodied subjects suggest that the EF in the proposed virtual hand rehabilitation platform is feasible, but it has not been tested on the limb-impaired subjects and confined to a manual encoding of electrical stimulation. In the future research, the design of a general EF enhanced hand rehabilitation platform with a standardized stimulation parameter optimization will be addressed and further validated on the subjects with limb impairments and amputation.

In this study we compared tactile and visual feedbacks for the motor imagery-based brain–computer interface (BCI) in five healthy subjects. A vertical green bar from the center of the fixing cross to the edge of the screen was used as visual feedback. Vibration motors that were placed on the forearms of the right and the left hands and on the back of the subject’s neck were used as tactile feedback. A vibration signal was used to confirm the correct classification of the EEG patterns of the motor imagery of right and left hand movements and the rest task. The accuracy of recognition in the classification of the three states (right hand movement, left hand movement, and rest) in the BCI without feedback exceeded the random level (33% for the three states) for all the subjects and was rather high (67.8% ± 13.4% (mean ± standard deviation)). Including the visual and tactile feedback in the BCI did not significantly change the mean accuracy of recognition of mental states for all the subjects (70.5% ± 14.8% for the visual feedback and 65.9% ± 12.4% for the tactile feedback). The analysis of the dynamics of the movement imagery skill in BCI users with the tactile and visual feedback showed no significant differences between these types of feedback. Thus, it has been found that the tactile feedback can be used in the motor imagery-based BCI instead of the commonly used visual feedback, which greatly expands the possibilities of the practical application of the BCI.

While user's perception and performance are predominantly examined independently in virtual reality, the Action-Specific Perception (ASP) theory postulates that the performance of an individual on a task modulates this individual's spatial and time perception pertinent to the task's components and procedures. This paper examines the association between performance and perception and the potential effects that tactile feedback modalities could generate. This paper reports a user study (N=24), in which participants performed a standardized Fitts's law target acquisition task by using three feedback modalities: visual, visuo-electrotactile, and visuo-vibrotactile. The users completed 3 Target Sizes × 2 Distances × 3 feedback modalities = 18 trials. The size perception, distance perception, and (movement) time perception were assessed at the end of each trial. Performance-wise, the results showed that electrotactile feedback facilitates a significantly better accuracy compared to vibrotactile and visual feedback, while vibrotactile provided the worst accuracy. Electrotactile and visual feedback enabled a comparable reaction time, while the vibrotactile offered a substantially slower reaction time than visual feedback. Although amongst feedback types the pattern of differences in perceptual aspects were comparable to performance differences, none of them was statistically significant. However, performance indeed modulated perception. Significant action-specific effects on spatial and time perception were detected. Changes in accuracy modulate both size perception and time perception, while changes in movement speed modulate distance perception. Also, the index of difficulty was found to modulate all three perceptual aspects. However, individual differences appear to affect the magnitude of action-specific effects. These outcomes highlighted the importance of haptic feedback on performance, and importantly the significance of action-specific effects on spatial and time perception in VR, which should be considered in future VR studies.

Virtual environments are often used in pre-wearing training and assessment of prosthetic control abilities. Here, we developed a virtual prosthetic hand training platform for evaluation of closed-loop control of grasp force. Biorealistic controllers emulated a pair of antagonistic muscles that actuated the thumb and index fingers of the hand. Surface electromyographic (sEMG) signals from a pair of antagonistic residual muscles drove the biorealistic controllers. Tactile forces from fingertip sensors were conveyed to amputees through evoked tactile sensations (ETS) elicited at the projected finger map (PFM) areas of the stump. A forearm amputee subject participated in force tracking or holding tasks using the virtual hand with residual muscle EMGs, or the contralateral intact hand. Root-mean-square error (RMSE) was used as outcome measure of motor performance. Results in this subject showed that the biorealistic controller enabled the virtual hand to track and maintain grasping forces. The best performance in both tasks was achieved by the contralateral intact hand with visual feedback. The roles of visual or tactile feedback in force tracking or maintaining were also assessed with the virtual hand. For force holding task, hybrid tactile and visual feedback with biorealistic control had a better performance than single visual or tactile feedback in terms of RMSE, success rate, and force variability. While in the force pursuing task, tactile feedback did not seem to add visual feedback in following the target force. The study suggests that training may be required for a novel virtual hand user to perceive and integrate multiple modalities of feedback information, so as to optimize the closed-loop control ability.

This research evaluated visual and haptic feedback for enhancing operator situational awareness during multitasking drone operations, investigating how task complexity influenced feedback effectiveness. Two virtual reality experiments were conducted: Study 1 assessed three types of visual feedback (Static, Blink, and Shake), while Study 2 compared visual, haptic, and visual-haptic multimodal feedback across varying task complexity levels determined by target density. Participants performed drone operation tasks requiring simultaneous obstacle avoidance and target counting. Results revealed that dynamic visual feedback (Shake) significantly improved primary task accuracy, reduced cognitive load, and was the preferred visual feedback type. Furthermore, haptic and visual-haptic feedback significantly shortened obstacle avoidance reaction times and decreased cognitive workload compared to visual feedback. Task complexity proved to be a critical factor: while visual feedback sufficed for low complexity scenarios, visual-haptic feedback offered distinct advantages in medium and high complexity tasks, providing insights for complexity-responsive drone interface design.

Tactile feedback from a remotely controlled robotic arm can facilitate certain tasks by enabling the user to experience tactile or force sensations from the robot's interaction with the environment. However, equipping both the robot and the user with tactile sensing and feedback systems can be complex, expensive, restrictive and application specific. This paper introduces a new tele-operation haptic feedback method involving electro-tactile feedback. This feedback system is inexpensive, easy to setup and versatile in that it can provide the user with a diverse range of tactile sensations and is suitable for a variety of tasks. We demonstrate the potential of our electro-tactile feedback system by providing experimental results showing how electro-tactile feedback from a tele-operated robotic arm equipped with range sensors can help with avoiding obstacles in cluttered workspace. We also show how interactive tasks, like placing a peg in a hole, can be facilitated with electro-tactile feedback from force sensors.

BackgroundMyoelectric interfaces have emerged as powerful tools for human–machine interaction (HMI), enabling intuitive control of virtual and physical devices. However, most existing systems are limited by low spatial resolution and unidirectional communication. To address these limitations, we developed NeuraLoop, a wearable, high-bandwidth, bidirectional interface that integrates myoelectric (EMG) signal acquisition and electrotactile stimulation feedback within a single wearable textile-based platform.MethodsNeuraLoop comprises a flexible matrix of 32 EMG recording and 32 electrotactile stimulation pads controlled by a compact electronic unit. We evaluated the system in two experimental tasks involving ten healthy subjects to demonstrate: (1) online classification of four transient thumb micro-gestures (thumb rightwards, leftwards, upwards, and downwards swipe directions), and (2) closed-loop control of a virtual cursor using micro-gesture commands and spatially encoded tactile feedback. A time-division multiplexing (TDM) strategy was implemented to enable simultaneous stimulation and recording.ResultsThe subjects achieved a median success rate of 82% on the first attempt and over 94% within two attempts during online classification with visual feedback. All four micro-gestures were classified with similar accuracy. In the closed-loop control task with tactile feedback, participants navigated a 3 × 4 grid using only electrotactile stimulation, achieving 70% accuracy for exact target hits and 95% when including the hits in the neighboring cells (1 cell distance error).ConclusionsNeuraLoop demonstrates the feasibility of high-bandwidth, bidirectional HMI using a wearable, textile-based interface. The system enables accurate recognition of subtle micro-gestures and effective delivery of spatially encoded tactile feedback. These capabilities open new possibilities for intuitive control in applications such as prosthetics, rehabilitation, and virtual/augmented reality. Future work will explore multimodal feedback encoding and proportional gesture control.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12984-025-01815-5.

Tactile feedback is pivotal for grasping and manipulation in humans. Providing functionally effective sensory feedback to prostheses users is an open challenge. Past paradigms were mostly based on vibro- or electrotactile stimulations. However, the tactile sensitivity on the targeted body parts (usually the forearm) is greatly less than that of the hand/fingertips, restricting the amount of information that can be provided through this channel. Visual feedback is the most investigated technique in motor learning studies, where it showed positive effects in learning both simple and complex tasks; however, it was not exploited in prosthetics due to technological limitations. Here, we investigated if visual information provided in the form of augmented reality (AR) feedback can be integrated by able-bodied participants in their sensorimotor control of a pick-and-lift task while controlling a robotic hand. For this purpose, we provided visual continuous feedback related to grip force and hand closure to the participants. Each variable was mapped to the length of one of the two ellipse axes visualized on the screen of wearable single-eye display AR glasses. We observed changes in behavior when subtle (i.e., not announced to the participants) manipulation of the AR feedback was introduced, which indicated that the participants integrated the artificial feedback within the sensorimotor control of the task. These results demonstrate that it is possible to deliver effective information through AR feedback in a compact and wearable fashion. This feedback modality may be exploited for delivering sensory feedback to amputees in a clinical scenario.

Visual vs vibrotactile feedback for posture assessment during upper-limb robot-aided rehabilitation

We introduce a novel posture guidance office chair and evaluate the effectiveness of vibrotactile and visual feedback methods for guiding seated postures. For visually dominant office work such as typing on the computer, it is possible that delivering posture feedback visually may overload the visual sense while haptic feedback may be a viable alternative. We performed two experiments to compare vibrotactile and visual feedback--posture compliance and dual-task cognitive workload assessment. In the first experiment, our results showed no statistically significant difference in effectiveness between using vibrotactile and visual feedback to obtain postural compliance to a reference posture. In the second experiment, participants experienced typing performance and response time degradations from both types of feedback. However the differences in performance degradation were not statistically significant between the two feedback methods. We conclude that vibrotactile and visual feedback are similarly effective for guiding quasistatic postures in routine tasks such as seated office work.

Virtual reality can help to learn basic laparoscopic tasks. However, no haptic feedback, which alerts for tissue slippage, is provided by most simulators, although, it might be of influence for the decrease of errors. This study explored whether visual or tactile feedback can be used to alert the surgeon of tissue slippage. Twenty-four participants performed a laparoscopic grasping task and where provided with either visual or tactile feedback about tissue slippage. The reaction time with the visual feedback was compared to the reaction time with tactile feedback signal. The results showed that when tissue slippage is simulated, tactile feedback shows significant faster reaction times (269ms) than visual feedback signals (398ms).

Fitts’ law models the relationship between amplitude, precision, and speed of rapid movements. It is widely used to quantify performance in pointing tasks, study human-computer interaction, and generally to understand perceptual-motor information processes, including research to model performance in isometric force production tasks. Applying Fitts’ law to an isometric grip force task would allow for quantifying grasp performance in rehabilitative medicine and may aid research on prosthetic control and design. We examined whether Fitts’ law would hold when participants attempted to accurately produce their intended force output while grasping a manipulandum when presented with images of various everyday objects (we termed this the implicit task). Although our main interest was the implicit task, to benchmark it and establish validity, we examined performance against a more standard visual feedback condition via a digital force-feedback meter on a video monitor (explicit task). Next, we progressed from visual force feedback with force meter targets to the same targets without visual force feedback (operating largely on feedforward control with tactile feedback). This provided an opportunity to see if Fitts’ law would hold without vision, and allowed us to progress toward the more naturalistic implicit task (which does not include visual feedback). Finally, we changed the nature of the targets from requiring explicit force values presented as arrows on a force-feedback meter (explicit targets) to the more naturalistic and intuitive target forces implied by images of objects (implicit targets). With visual force feedback the relation between task difficulty and the time to produce the target grip force was predicted by Fitts’ law (average r2 = 0.82). Without vision, average grip force scaled accurately although force variability was insensitive to the target presented. In contrast, images of everyday objects generated more reliable grip forces without the visualized force meter. In sum, population means were well-described by Fitts’ law for explicit targets with vision (r2 = 0.96) and implicit targets (r2 = 0.89), but not as well-described for explicit targets without vision (r2 = 0.54). Implicit targets should provide a realistic see-object-squeeze-object test using Fitts’ law to quantify the relative speed-accuracy relationship of any given grasper.

Robot-Assisted Gait Training (RAGT) is a widely adopted neurorehabilitation approach for individuals with Spinal Cord Injury (SCI). In this context, active patient participation is a key factor to improve the success of the therapy. Previous works with the robotic trainer Lokomat have shown that realtime visual feedback and verbal instructions from a physical therapist effectively enhance active participation. However, the optimal type of visual feedback and its potential synergy with verbal indications remain unclear. This study investigates how different visual feedback types influence active participation when combined with therapist verbal instructions. Nine participants with SCI underwent RAGT sessions with three types of visual feedback (chart, emoticon, game) and two levels of patienttherapist verbal interaction (high, low). Active participation was quantified from a biomechanical point of view through the human-robot interaction work in four joints (left/right hip and knee) during the stance and swing phases of the gait cycle. Results showed that feedback chart was the most effective in promoting positive, symmetric performance, as opposed to feedback game and emoticon which, though probably more engaging, exhibited heterogeneous results. High therapist interaction significantly improved the performance of the limb with the highest muscular strength for the hip during the swing phase, whereas the effects on the limb with the lowest muscular strength were less clear.

A new method of investigating the ergonomics of visual cardiopulmonary resuscitation (CPR) feedback delivery

This paper describes development of an activity based, multimodal balance rehabilitation training device. Various sensors can be used, including a force plate, inertial sensors, and depth sensing cameras, and various combinations of visual, auditory and tactile feedback can be configured depending on the rehabilitation task and activity. Tactile feedback is presented via a lightweight belt that is worn on the torso. Generally, visual feedback is only needed at the start of rehabilitation training (task orientation) while tactile feedback may be used to augment balance control. Tactile feedback can be configured as a cue that certain movement targets or limits have been reached or as an immediate indicator of the variance in postural sway. Tactile feedback allows the subject to naturally concentrate on the functional rehabilitation task, and is less reliant on visual or verbal cues.