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Background: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions., Methods: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback., Results: Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants., Conclusions: The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use., (© 2024. The Author(s).)

Objective. Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process. Approach. Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined. Main results. We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill. Significance. We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control., (Creative Commons Attribution license.)

Objective: The bidirectional communication between the user and the prosthesis is an important requirement when developing prosthetic hands. Proprioceptive feedback is fundamental to perceiving prosthesis movement without the need for constant visual attention. We propose a novel solution to encode wrist rotation using a vibromotor array and Gaussian interpolation of vibration intensity. The approach generates tactile sensation that smoothly rotates around the forearm congruently with prosthetic wrist rotation. The performance of this scheme was systematically assessed for a range of parameter values (number of motors and Gaussian standard deviation)., Methods: Fifteen non-disabled subjects and one individual with congenital limb deficiency used vibrational feedback to control a virtual hand in the target-achievement control test. Performance was assessed by end-point error and efficiency as well as subjective impressions., Results: The results showed a preference for smooth feedback and a higher number of motors (8 and 6 versus 4). With 8 and 6 motors, the standard deviation, determining the sensation spread and continuity, could be modulated through a broad range of values (0.1 - 2) without a significant performance loss. The overall average error and efficiency across these feedback configurations were ∼ 10% and ∼ 30%, respectively. For low values of standard deviation (0.1-0.5), the number of motors could be reduced to 4 without a significant performance decrease., Conclusion: The study demonstrated that the developed strategy provided meaningful rotation feedback. Moreover, the results indicate that the Gaussian standard deviation could be used as an independent parameter to encode an additional feedback variable., Significance: The proposed method is a flexible and effective approach to provide proprioceptive feedback while adjusting the trade-off between sensation quality and the number of vibromotors.

Bidirectional human-machine interfaces involve commands from the central nervous system to an external device and feedback characterizing device state. Such feedback may be elicited by electrical stimulation of somatosensory nerves, where a task-relevant variable is encoded in stimulation amplitude or frequency. Recently, concurrent modulation in amplitude and frequency (multimodal encoding) was proposed. We hypothesized that feedback with multimodal encoding may effectively be processed by the central nervous system as two independent inputs encoded in amplitude and frequency, respectively, thereby increasing state estimate quality in accordance with maximum-likelihood estimation. Using an adaptation paradigm, we tested this hypothesis during a grasp force matching task where subjects received electrotactile feedback encoding instantaneous force in amplitude, frequency, or both, in addition to their natural force feedback. The results showed that adaptations in grasp force with multimodal encoding could be accurately predicted as the integration of three independent inputs according to maximum-likelihood estimation: amplitude modulated electrotactile feedback, frequency modulated electrotactile feedback, and natural force feedback (r 2  = 0.73). These findings show that multimodal electrotactile feedback carries an intrinsic advantage for state estimation accuracy with respect to single-variable modulation and suggest that this scheme should be the preferred strategy for bidirectional human-machine interfaces with electrotactile feedback., (© 2023. The Author(s).)

Objective . Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed-accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces. Approach . Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a 1 week washout period. Importantly, both interfaces used direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). We estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions. Main results . We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands. Significance . Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed-accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces., (© 2022 IOP Publishing Ltd.)

Electrotactile stimulation has been commonly used in human-machine interfaces to provide feedback to the user, thereby closing the control loop and improving performance. The encoding approach, which defines the mapping of the feedback information into stimulation profiles, is a critical component of an electrotactile interface. Ideally, the encoding will provide a high-fidelity representation of the feedback variable while being easy to perceive and interpret by the subject. In the present study, we performed a closed-loop experiment wherein discrete and continuous coding schemes are combined to exploit the benefits of both techniques. Subjects performed a muscle activation-matching task relying solely on electrotactile feedback representing the generated myoelectric signal (EMG). In particular, we investigated the performance of two different coding schemes (spatial and spatial combined with frequency) at two feedback resolutions (low: 3 and high: 5 intervals). In both schemes, the stimulation electrodes were placed circumferentially around the upper arm. The magnitude of the normalized EMG was divided into intervals, and each electrode was associated with one interval. When the generated EMG entered one of the intervals, the associated electrode started stimulating. In the combined encoding, the additional frequency modulation of the active electrode also indicated the momentary magnitude of the signal within the interval. The results showed that combined coding decreased the undershooting rate, variability and absolute deviation when the resolution was low but not when the resolution was high, where it actually worsened the performance. This demonstrates that combined coding can improve the effectiveness of EMG feedback, but that this effect is limited by the intrinsic variability of myoelectric control. Our findings, therefore, provide important insights as well as elucidate limitations of the information encoding methods when using electrotactile stimulation to convey a feedback signal characterized by high variability (EMG biofeedback)., (© 2022. The Author(s).)

Tactile feedback is relevant in a broad range of human-machine interaction systems (e.g. teleoperation, virtual reality and prosthetics). The available tactile feedback interfaces comprise few sensing and stimulation units, which limits the amount of information conveyed to the user. The present study describes a novel technology that relies on distributed sensing and stimulation to convey comprehensive tactile feedback to the user of a robotic end effector. The system comprises six flexible sensing arrays (57 sensors) integrated on the fingers and palm of a robotic hand, embedded electronics (64 recording channels), a multichannel stimulator and seven flexible electrodes (64 stimulation pads) placed on the volar side of the subject's hand. The system was tested in seven subjects asked to recognize contact positions and identify contact sliding on the electronic skin, using distributed anode configuration (DAC) and single dedicated anode configuration. The experiments demonstrated that DAC resulted in substantially better performance. Using DAC, the system successfully translated the contact patterns into electrotactile profiles that the subjects could recognize with satisfactory accuracy ([Formula: see text] for static and [Formula: see text] for dynamic patterns). The proposed system is an important step towards the development of a high-density human-machine interfacing between the user and a robotic hand. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Electro- and vibro-tactile stimulation are commonly employed for feedback in closed-loop human-machine interfacing. Although these feedback systems have been extensively investigated individually, they are rarely objectively compared. In this study, two state-of-the-art stimulation units (concentric electrode and C2-tactor) similar in shape and size were compared in psychometric and online control tests. The just noticeable difference and number of discriminable levels for intensity and frequency modulation were determined across values of carrier frequency and intensity, respectively. Next, subjects performed a compensatory tracking task, in which the feedback encoded the momentary tracking error. In the psychometric tests, intensity modulation outperformed frequency modulation and electrotactile stimulation enabled significantly higher resolution than vibrotactile stimulation, for the same carrier frequency. However, for the best-case settings (eletro-tactile: 100 Hz; vibro-tactile: 200 Hz), the two stimulation modalities were equivalent in the psychometric tests and in the online control tests, where the two stimulation methods resulted in similar correlation and deviation between the target and the generated trajectory. Time delay was slightly but significantly lower for the vibrotactile modality. Overall, the present assessment shows that despite psychometric differences between the two stimulation methods, they enable similar online control performance when parameters are optimally selected for each modality.

As the technology moves towards more human-like bionic limbs, it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units, and hence they are characterized with a limited amount of information that can be transmitted by the feedback. The present study describes a novel system for tactile feedback integrating advanced multipoint sensing (electronic skin) and stimulation (matrix electrodes). The system comprises a flexible sensing array (16 sensors) integrated on the index finger of a Michelangelo prosthetic hand mockup, embedded interface electronics and multichannel stimulator connected to a flexible matrix electrode (24 pads). The developed system conveys contact information (binary detections) to the user. To demonstrate the feasibility, the system was tested in six able-bodied subjects who were asked to recognize static patterns (contact position) with two different spatial resolutions and dynamic movement patterns (i.e., sliding along and/or across the finger) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles, which the subjects could recognize with good performance. The success rates (mean ± standard deviation) for the static patterns were 91 ± 4% and 58 ± 10% for low and high spatial resolution, respectively, while the success rate for sliding touch was 94 ± 4%. These results demonstrate that the developed system is an important step towards a new generation of tactile feedback interfaces that can provide high-bandwidth connection between the user and his/her bionic limb. Such systems would allow mimicking spatially distributed natural feedback, thereby facilitating the control and embodiment of the artificial device into the user body scheme.

Objective. The implementation of somatosensory feedback in upper limb myoelectric prostheses is an important step towards the restoration of lost sensory-motor functions. EMG feedback is a recently proposed method for closing the control loop wherein the myoelectric signal that drives the prosthesis is also used to generate the feedback provided to the user. Therefore, the characteristics of the myoelectric signal (variability and sensitivity) are likely to significantly affect the ability of the subject to utilize this feedback for online control of the prosthesis. Approach. In the present study, we investigated how the cutoff frequency of the low-pass filter (0.5, 1 and 1.5 Hz) and normalization value (20%, 40% and 60% of the maximum voluntary contraction (MVC)), that are used for the generation of the myoelectric signal, affect the quality of closed-loop control with EMG feedback. Lower cutoff and normalization decrease the intrinsic variability of the EMG but also increase the time lag between the contraction and the feedback (cutoff) as well as the sensitivity of the myoelectric signal (normalization). Ten participants were asked to generate three grasp force levels with a myoelectric prosthetic hand, while receiving five-level vibrotactile EMG feedback, over nine experimental runs (all parameter combinations). Main results. The outcome measure was the success rate (SR) in achieving the appropriate level of myoelectric signal (primary outcome) and grasping force (secondary outcome). Overall, the experiments demonstrated that EMG feedback provided robust control across conditions. Nevertheless, the performance was significantly better for the lowest cutoff (0.5 Hz) and higher normalization (40% and 60%). The highest SR for the EMG was 71.9%, achieved in the condition (40% MVC and 0.5 Hz), and this was 24.1% higher than that in the condition (20% MVC and 1.5 Hz), which resulted in the lowest performance. The SR for the force followed a similar trend. Significance. This is the first study that systematically explored the parameter space for the calibration of EMG feedback, which is a critical step for the future clinical application of this approach., (© 2021 IOP Publishing Ltd.)

Sensory substitution by electrotactile stimulation has been widely investigated for improving the functionality of human-machine interfaces. Few studies, however, have objectively compared different ways in which such systems can be implemented. In this study, we compare encoding of a feedback variable in stimulation pulse width or stimulation frequency during a closed-loop control task. Specifically, participants were asked to track a predefined pseudorandom trajectory using a joystick with electrotactile feedback as the only indication of the tracking error. Each participant performed eight 90 s trials per encoding scheme. Tracking performance using frequency modulation enabled lower tracking error (RMSE: Frequency modulation: 0.27 ± 0.03; Pulse width modulation: 0.31 ± 0.05; p < 0.05) and a higher correlation with the target trajectory (Frequency modulation: 83.4 ± 4.1%; Pulse width modulation: 79.8 ± 5.2%; p < 0.05). There was no significant improvement in performance over the eight trials. Furthermore, frequency-domain analysis revealed that frequency modulation was characterized with a higher gain at lower error frequencies. In summary, the results indicate that encoding of feedback variables in the frequency of pulses enables better control than pulse width modulation in closed-loop dynamic tasks.

While prosthetic fitting after upper-limb loss allows for restoration of motor functions, it deprives the amputee of tactile sensations that are essential for grasp control in able-bodied subjects. Therefore, it is commonly assumed that restoring the force feedback would improve the control of prosthesis grasping force. However, the literature regarding the benefit of feedback is controversial. Here, we investigated how the type of feedback affects learning and steady-state performance of routine grasping with a prosthesis. The experimental task was to grasp an object using a prosthesis and generate a low or high target-force range (TFR), both initially unknown, in three feedback conditions: basic auditory feedback on task outcome, and additional visual or vibratory feedback on the force magnitude. The results demonstrated that the performance was rather good and stable for the low TFR, whereas it was substantially worse for the high TFR with a pronounced training effect. Surprisingly, learning curve and steady-state performance did not depend on the feedback condition. Hence, in the specific context of routing grasping with a prosthesis controlled via surface EMG, the basic feedback on task outcome was not outperformed by force-related end-of-trial feedback and hence seemed to be sufficient for accomplishing the task.This conclusion applies to the context of routine grasping using a myoelectric prosthesis with surface EMG electrodes, which means that the control signals are variable and the feedback is perceived and processed at the end of the trial (motor adaption).

Among most challenging open issues in prosthetic research is the development of a robust bidirectional interface between a prosthesis and its user. Commercially available prosthetic systems are mechanically advanced, but they do not provide somatosensory feedback. Here, we present a novel non-invasive interface for multichannel electrotactile feedback, comprising a matrix of 24 pads, and we investigate the ability of able-bodied human subjects to localize the electrotactile stimulus delivered through the matrix. For this purpose, we tested conventional stimulation (same frequency for all pads) and a novel dual-parameter modulation scheme (interleaved frequency and intensity) designed to facilitate the spatial localization over the electrode. Electrotactile stimulation was also compared to mechanical stimulation of the same locations on the skin. Experimental results on eight able-bodied subjects demonstrated that the proposed interleaved coding substantially improved the spatial localization compared to same-frequency stimulation. The results also showed that same-frequency stimulation was equivalent to mechanical stimulation, whereas the performance with dual-parameter modulation was significantly better. These are encouraging outcomes for the application of a multichannel interface for the restoration of feedback in prosthetics. The high-resolution augmented interfaces might be used to explore novel scenarios for effective communication with the prosthesis user enabled by maximizing information transmission.

Electrotactile stimulation has been suggested as a modality for providing sensory feedback in upper limb prostheses. This study investigates the multiday variability of subdermal and surface stimulation. Electrical stimulation was delivered using either surface or fine wire electrodes placed right under the skin in eight amputees for seven consecutive days. The variability of psychophysical measurements, including detection threshold (DT), pain threshold (PT), dynamic range (DR), just noticeable difference (JND), Weber fraction (WF) and quality of evoked sensations, was evaluated using the coefficient of variation (CoV). In addition, the systematic change in the mean of the parameters across days was assessed in both stimulation modalities. In the case of DT, PT, DR, and perceived intensity at 100 Hz, the CoV of surface stimulation was significantly smaller than that of subdermal stimulation. Only PT showed a significant systematic change in the mean value across days for both modalities. The outcome of this study has implications for the choice of modality in delivering sensory feedback, though the significance of the quantified variability needs to be evaluated using usability tests with user feedback.

Background: A prosthetic system should ideally reinstate the bidirectional communication between the user's brain and its end effector by restoring both motor and sensory functions lost after an amputation. However, current commercial prostheses generally do not incorporate somatosensory feedback. Even without explicit feedback, grasping using a prosthesis partly relies on sensory information. Indeed, the prosthesis operation is characterized by visual and sound cues that could be exploited by the user to estimate the prosthesis state. However, the quality of this incidental feedback has not been objectively evaluated., Methods: In this study, the psychometric properties of the auditory and visual feedback of prosthesis motion were assessed and compared to that of a vibro-tactile interface. Twelve able-bodied subjects passively observed prosthesis closing and grasping an object, and they were asked to discriminate (experiment I) or estimate (experiment II) the closing velocity of the prosthesis using visual (VIS), acoustic (SND), or combined (VIS + SND) feedback. In experiment II, the subjects performed the task also with a vibrotactile stimulus (VIB) delivered using a single tactor. The outcome measures for the discrimination and estimation experiments were just noticeable difference (JND) and median absolute estimation error (MAE), respectively., Results: The results demonstrated that the incidental sources provided a remarkably good discrimination and estimation of the closing velocity, significantly outperforming the vibrotactile feedback. Using incidental sources, the subjects could discriminate almost the minimum possible increment/decrement in velocity that could be commanded to the prosthesis (median JND < 2% for SND and VIS + SND). Similarly, the median MAE in estimating the prosthesis velocity randomly commanded from the full working range was also low, i.e., approximately 5% in SND and VIS + SND., Conclusions: Since the closing velocity is proportional to grasping force in state-of-the-art myoelectric prostheses, the results of the present study imply that the incidental feedback, when available, could be usefully exploited for grasping force control. Therefore, the impact of incidental feedback needs to be considered when designing a feedback interface in prosthetics, especially since the quality of estimation using supplemental sources (e.g., vibration) can be worse compared to that of the intrinsic cues.

Background: Sensory feedback is critical for grasping in able-bodied subjects. Consequently, closing the loop in upper-limb prosthetics by providing artificial sensory feedback to the amputee is expected to improve the prosthesis utility. Nevertheless, even though amputees rate the prospect of sensory feedback high, its benefits in daily life are still very much debated. We argue that in order to measure the potential functional benefit of artificial sensory feedback, the baseline open-loop performance needs to be established., Methods: The myoelectric control of naïve able-bodied subjects was evaluated during modulation of electromyographic signals (EMG task), and grasping with a prosthesis (Prosthesis task). The subjects needed to activate the wrist flexor muscles and close the prosthesis to reach a randomly selected target level (routine grasping). To assess the baseline performance, the tasks were performed with a different extent of implicit feedback (proprioception, prosthesis motion and sound). Finally, the prosthesis task was repeated with explicit visual force feedback. The subjects' ability to scale the prosthesis command/force was assessed by testing for a statistically significant increase in the median of the generated commands/forces between neighboring levels. The quality of control was evaluated by computing the median absolute error (MAE) with respect to the target., Results: The subjects could successfully scale their motor commands and generated prosthesis forces across target levels in all tasks, even with the least amount of implicit feedback (only muscle proprioception, EMG task). In addition, the deviation of the generated commands/forces from the target levels decreased with additional feedback. However, the increase in implicit feedback, from proprioception to prosthesis motion and sound, seemed to have a more substantial effect than the final introduction of explicit feedback. Explicit feedback improved the performance mainly at the higher target-force levels., Conclusions: The study establishes the baseline performance of myoelectric control and prosthesis grasping force. The results demonstrate that even without additional feedback, naïve subjects can effectively modulate force with good accuracy with respect to that achieved when increasing the amount of feedback information.

Background: To effectively replace the human hand, a prosthesis should seamlessly respond to user intentions but also convey sensory information back to the user. Restoration of sensory feedback is rated highly by the prosthesis users, and feedback is critical for grasping in able-bodied subjects. Nonetheless, the benefits of feedback in prosthetics are still debated. The lack of consensus is likely due to the complex nature of sensory feedback during prosthesis control, so that its effectiveness depends on multiple factors (e.g., task complexity, user learning)., Methods: We evaluated the impact of these factors with a longitudinal assessment in six amputee subjects, using a clinical setup (socket, embedded control) and a range of tasks (box and blocks, block turn, clothespin and cups relocation). To provide feedback, we have proposed a novel vibrotactile stimulation scheme capable of transmitting multiple variables from a multifunction prosthesis. The subjects wore a bracelet with four by two uniformly placed vibro-tactors providing information on contact, prosthesis state (active function), and grasping force. The subjects also completed a questionnaire for the subjective evaluation of the feedback., Results: The tests demonstrated that feedback was beneficial only in the complex tasks (block turn, clothespin and cups relocation), and that the training had an important, task-dependent impact. In the clothespin relocation and block turn tasks, training allowed the subjects to establish successful feedforward control, and therefore, the feedback became redundant. In the cups relocation task, however, the subjects needed some training to learn how to properly exploit the feedback. The subjective evaluation of the feedback was consistently positive, regardless of the objective benefits. These results underline the multifaceted nature of closed-loop prosthesis control as, depending on the context, the same feedback interface can have different impact on performance. Finally, even if the closed-loop control does not improve the performance, it could be beneficial as it seems to improve the subjective experience., Conclusions: Therefore, in this study we demonstrate, for the first time, the relevance of an advanced, multi-variable feedback interface for dexterous, multi-functional prosthesis control in a clinically relevant setting.

This paper evaluated the psychophysical properties of subdermal electrical stimulation to investigate its feasibility in providing sensory feedback for limb prostheses. The detection threshold (DT), pain threshold (PT), just noticeable difference (JND), as well as the elicited sensation quality, comfort, intensity, and location were assessed in 16 healthy volunteers during stimulation of the ventral and dorsal forearm with subdermal electrodes. Moreover, the results were compared with those obtained from transcutaneous electrical stimulation. Despite a lower DT and PT, subdermal stimulation attained a greater relative dynamic range (i.e., PT/DT) and significantly smaller JNDs for stimulation amplitude. Muscle twitches and movements were more commonly elicited by surface stimulation, especially at the higher stimulation frequencies, whereas the pinprick sensation was more often reported with subdermal stimulation. Less comfort was perceived in subdermal stimulation of the ventral forearm at the highest tested stimulation frequency of 100 Hz. In summary, subdermal electrical stimulation was demonstrated to be able to produce similar sensation quality as transcutaneous stimulation and outperformed the latter in terms of energy efficiency and sensitivity. These results suggest that stimulation through implantable subdermal electrodes may lead to an efficient and compact sensory feedback system for substituting the lost sense in amputees.

Human motor control relies on a combination of feedback and feedforward strategies. The aim of this study was to longitudinally investigate artificial somatosensory feedback and feedforward control in the context of grasping with myoelectric prosthesis. Nine amputee subjects performed routine grasping trials, with the aim to produce four levels of force during four blocks of 60 trials across five days. The electrotactile force feedback was provided in the second and third block using multipad electrode and spatial coding. The first baseline and last validation block (open-loop control) evaluated the effects of long- (across sessions) and short-term (within session) learning, respectively. The outcome measures were the absolute error between the generated and target force, and the number of force saturations. The results demonstrated that the electrotactile feedback improved the performance both within and across sessions. In the validation block, the performance did not significantly decrease and the quality of open-loop control (baseline) improved across days, converging to the performance characterizing closed-loop control. This paper provides important insights into the feedback and feedforward processes in prosthesis control, contributing to the better understanding of the role and design of feedback in prosthetic systems.

Grasping is a complex task routinely performed in an anticipatory (feedforward) manner, where sensory feedback is responsible for learning and updating the internal model of grasp dynamics. This study aims at evaluating whether providing a proportional tactile force feedback during the myoelectric control of a prosthesis facilitates learning a stable internal model of the prosthesis force control. Ten able-bodied subjects controlled a sensorized myoelectric prosthesis performing four blocks of consecutive grasps at three levels of target force (30, 50, and 70%), repeatedly closing the fully opened hand. In the first and third block, the subjects received tactile and visual feedback, respectively, while during the second and fourth block, the feedback was removed. The subjects also performed an additional block with no feedback 1 day after the training (Retest). The median and interquartile range of the generated forces was computed to assess the accuracy and precision of force control. The results demonstrated that the feedback was indeed an effective instrument for the training of prosthesis control. After the training, the subjects were still able to accurately generate the desired force for the low and medium target (30 and 50% of maximum force available in a prosthesis), despite the feedback being removed within the session and during the retest (low target force). However, the training was substantially less successful for high forces (70% of prosthesis maximum force), where subjects exhibited a substantial loss of accuracy as soon as the feedback was removed. The precision of control decreased with higher forces and it was consistent across conditions, determined by an intrinsic variability of repeated myoelectric grasping. This study demonstrated that the subject could rely on the tactile feedback to adjust the motor command to the prosthesis across trials. The subjects adjusted the mean level of muscle activation (accuracy), whereas the precision could not be modulated as it depends on the intrinsic myoelectric variability. They were also able to maintain the feedforward command even after the feedback was removed, demonstrating thereby a stable learning, but the retention depended on the level of the target force. This is an important insight into the role of feedback as an instrument for learning of anticipatory prosthesis force control.

Objective: Providing sensory feedback to the user of the prosthesis is an important challenge. The common approach is to use tactile stimulation, which is easy to implement but requires training and has limited information bandwidth. In this study, we propose an alternative approach based on augmented reality., Approach: We have developed the GLIMPSE, a Google Glass application which connects to the prosthesis via a Bluetooth interface and renders the prosthesis states (EMG signals, aperture, force and contact) using augmented reality (see-through display) and sound (bone conduction transducer). The interface was tested in healthy subjects that used the prosthesis with (FB group) and without (NFB group) feedback during a modified clothespins test that allowed us to vary the difficulty of the task. The outcome measures were the number of unsuccessful trials, the time to accomplish the task, and the subjective ratings of the relevance of the feedback., Main Results: There was no difference in performance between FB and NFB groups in the case of a simple task (basic, same-color clothespins test), but the feedback significantly improved the performance in a more complex task (pins of different resistances). Importantly, the GLIMPSE feedback did not increase the time to accomplish the task. Therefore, the supplemental feedback might be useful in the tasks which are more demanding, and thereby less likely to benefit from learning and feedforward control. The subjects integrated the supplemental feedback with the intrinsic sources (vision and muscle proprioception), developing their own idiosyncratic strategies to accomplish the task., Significance: The present study demonstrates a novel self-contained, ready-to-deploy, wearable feedback interface. The interface was successfully tested and was proven to be feasible and functionally beneficial. The GLIMPSE can be used as a practical solution but also as a general and flexible instrument to investigate closed-loop prosthesis control.

Myoelectric prostheses are successfully controlled using muscle electrical activity, thereby restoring lost motor functions. However, the somatosensory feedback from the prosthesis to the user is still missing. The sensory substitution methods described in the literature comprise mostly simple position and force sensors combined with discrete stimulation units. The present study describes a novel system for sophisticated electrotactile feedback integrating advanced distributed sensing (electronic skin) and stimulation (matrix electrodes). The system was tested in eight healthy subjects who were asked to recognize the shape, trajectory, and direction of a set of dynamic movement patterns (single lines, geometrical objects, letters) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into the moving electrotactile profiles, which the subjects could recognize with a good performance (shape recognition: 86±8% lines, 73±13% geometries, 72±12% letters). In particular, the subjects could identify the movement direction with a high confidence. These results are in accordance with previous studies investigating the recognition of moving stimuli in human subjects. This is an important development towards closed-loop prostheses providing comprehensive and sophisticated tactile feedback to the user, facilitating the control and the embodiment of the artificial device into the user body scheme.

Providing somatosensory feedback to the user of a myoelectric prosthesis is an important goal since it can improve the utility as well as facilitate the embodiment of the assistive system. Most often, the grasping force was selected as the feedback variable and communicated through one or more individual single channel stimulation units (e.g., electrodes, vibration motors). In the present study, an integrated, compact, multichannel solution comprising an array electrode and a programmable stimulator was presented. Two coding schemes (15 levels), spatial and mixed (spatial and frequency) modulation, were tested in able-bodied subjects, psychometrically and in force control with routine grasping and force tracking using real and simulated prosthesis. The results demonstrated that mixed and spatial coding, although substantially different in psychometric tests, resulted in a similar performance during both force control tasks. Furthermore, the ideal, visual feedback was not better than the tactile feedback in routine grasping. To explain the observed results, a conceptual model was proposed emphasizing that the performance depends on multiple factors, including feedback uncertainty, nature of the task and the reliability of the feedforward control. The study outcomes, specific conclusions and the general model, are relevant for the design of closed-loop myoelectric prostheses utilizing tactile feedback.

Objective: A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate., Approach: In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively., Main Results: Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements., Significance: Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for predictive control, as the subjects used the feedback to adjust the desired force even before the prosthesis contacted the object. In conclusion, the online emgFB was superior to the classic forceFB in realistic conditions that included electrotactile stimulation, limited feedback resolution (8 levels), cognitive processing delay, and time constraints (fast grasping).

Objective: Closing the loop in myoelectric prostheses by providing artificial somatosensory feedback to the user is an important need for prosthetic users. Previous studies investigated feedback strategies in combination with the control of one degree of freedom of simple grippers. Modern hands, however, are sophisticated multifunction systems. In this study, we assessed multichannel electrotactile feedback integrated with an advanced method for the simultaneous and proportional control of individual fingers of a dexterous hand., Approach: The feedback used spatial and frequency coding to provide information on the finger positions (normalized flexion angles). A comprehensive set of conditions have been investigated in 28 able-bodied subjects, including feedback modalities (visual, electrotactile and no feedback), control tasks (fingers and grasps), systems (virtual and real hand), control methods (ideal and realistic) and range of motion (low and high). The task for the subjects was to operate the hand using closed-loop myoelectric control and generate the desired movement (e.g., selected finger or grasp at a specific level of closure)., Main Results: The subjects could perceive the multichannel and multivariable electrotactile feedback and effectively exploit it to improve the control performance with respect to open-loop grasping. The improvement however depended on the reliability of the feedforward control, with less consistent control exhibiting performance trends that were more complex across the conditions., Significance: The results are promising for the potential application of advanced feedback to close the control loop in sophisticated prosthetic systems.

Electrocutaneous stimulation is a promising approach to provide sensory feedback to amputees, and thus close the loop in upper limb prosthetic systems. However, the stimulation introduces artifacts in the recorded electromyographic (EMG) signals, which may be detrimental for the control of myoelectric prostheses. In this study, artifact blanking with three data segmentation approaches was investigated as a simple method to restore the performance of pattern recognition in prosthesis control (eight motions) when EMG signals are corrupted by stimulation artifacts. The methods were tested over a range of stimulation conditions and using four feature sets, comprising both time and frequency domain features. The results demonstrated that when stimulation artifacts were present, the classification performance improved with blanking in all tested conditions. In some cases, the classification performance with blanking was at the level of the benchmark (artifact-free data). The greatest pulse duration and frequency that allowed a full performance recovery were 400 μs and 150 Hz, respectively. These results show that artifact blanking can be used as a practical solution to eliminate the negative influence of the stimulation artifact on EMG pattern classification in a broad range of conditions, thus allowing to close the loop in myoelectric prostheses using electrotactile feedback.

Background: Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback)., Methods: As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task., Results: During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees., Conclusions: The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.

Prosthesis users usually agree that myoelectric prostheses should be equipped with somatosensory feedback. However, the exact role of feedback and potential benefits are still elusive. The current study investigates the nature of human control processes within a specific context of routine grasping. Although the latter includes a fast feedforward control of the grasping force, the assumption was that the feedback would still be useful; it would communicate the outcome of the grasping trial, which the subjects could use to learn an internal model of feedforward control. Nine able-bodied subjects produced repeatedly a desired level of grasping force using different control configurations: feedback versus no-feedback, virtual versus real prosthetic hand, and joystick versus myocontrol. The outcome measures were the median and dispersion of the relative force errors. The results demonstrated that the feedback was successful in limiting the variability of the routine grasping due to uncertainties in the system and/or the command interface. The internal models of feedforward control could be employed by the subjects to control the prosthesis without the loss of performance even after the force feedback was removed. The models were, however, unstable over time, especially with myocontrol. Overall, the study demonstrates that the prosthesis system can be learned by the subjects using feedback. The feedback is also essential to maintain the model, and it could be delivered intermittently. This approach has practical advantages, but the level to which this mechanism can be truly exploited in practice depends directly on the consistency of the prosthesis control interface.

Background: Electrocutaneous stimulation can restore the missing sensory information to prosthetic users. In electrotactile feedback, the information about the prosthesis state is transmitted in the form of pulse trains. The stimulation frequency is an important parameter since it influences the data transmission rate over the feedback channel as well as the form of the elicited tactile sensations., Methods: We evaluated the influence of the stimulation frequency on the subject's ability to utilize the feedback information during electrotactile closed-loop control. Ten healthy subjects performed a real-time compensatory tracking (standard test bench) of sinusoids and pseudorandom signals using either visual feedback (benchmark) or electrocutaneous feedback in seven conditions characterized by different combinations of the stimulation frequency (FSTIM) and tracking error sampling rate (FTE). The tracking error was transmitted using two concentric electrodes placed on the forearm. The quality of tracking was assessed using the Squared Pearson Correlation Coefficient (SPCC), the Normalized Root Mean Square Tracking Error (NRMSTE) and the time delay between the reference and generated trajectories (TDIO)., Results: The results demonstrated that FSTIM was more important for the control performance than FTE. The quality of tracking deteriorated with a decrease in the stimulation frequency, SPCC and NRMSTE (mean) were 87.5% and 9.4% in the condition 100/100 (FTE/FSTIM), respectively, and deteriorated to 61.1% and 15.3% in 5/5, respectively, while the TDIO increased from 359.8 ms in 100/100 to 1009 ms in 5/5. However, the performance recovered when the tracking error sampled at a low rate was delivered using a high stimulation frequency (SPCC = 83.6%, NRMSTE = 10.3%, TDIO = 415.6 ms, in 5/100)., Conclusions: The likely reason for the performance decrease and recovery was that the stimulation frequency critically influenced the tactile perception quality and thereby the effective rate of information transfer through the feedback channel. The outcome of this study can facilitate the selection of optimal system parameters for somatosensory feedback in upper limb prostheses. The results imply that the feedback variables (e.g., grasping force) should be transmitted at relatively high frequencies of stimulation (>25 Hz), but that they can be sampled at much lower rates (e.g., 5 Hz).

Closing the control loop by providing sensory feedback to the user of a prosthesis is an important challenge, with major impact on the future of prosthetics. Developing and comparing closed-loop systems is a difficult task, since there are many different methods and technologies that can be used to implement each component of the system. Here, we present a test bench developed in Matlab Simulink for configuring and testing the closed-loop human control system in standardized settings. The framework comprises a set of connected generic blocks with normalized inputs and outputs, which can be customized by selecting specific implementations from a library of predefined components. The framework is modular and extensible and it can be used to configure, compare and test different closed-loop system prototypes, thereby guiding the development towards an optimal system configuration. The use of the test bench was demonstrated by investigating two important aspects of closed-loop control: performance of different electrotactile feedback interfaces (spatial versus intensity coding) during a pendulum stabilization task and feedforward methods (joystick versus myocontrol) for force control. The first experiment demonstrated that in the case of trained subjects the intensity coding might be superior to spatial coding. In the second experiment, the control of force was rather poor even with a stable and precise control interface (joystick), demonstrating that inherent characteristics of the prosthesis can be an important limiting factor when considering the overall effectiveness of the closed-loop control. The presented test bench is an important instrument for investigating different aspects of human manual control with sensory feedback.

Background: Restoring sensory feedback in myoelectric prostheses is still an open challenge. Closing the loop might lead to a more effective utilization and better integration of these systems into the body scheme of the user. Electrotactile stimulation can be employed to transmit the feedback information to the user, but it represents a strong interference to the recording of the myoelectric signals that are used for control. Time-division multiplexing (TDM) can be applied to avoid this interference by performing the stimulation and recording in dedicated, non-overlapping time windows., Methods: A closed-loop compensatory tracking task with myocontrol and electrotactile stimulation was used to investigate how the duration of the feedback window (FW) influences the ability to perceive the feedback information and react with an appropriate control action. Nine subjects performed eight trials with continuous recording and contralateral feedback (CONT-CLT) and TDM with ispilateral stimulation and recording using the FW of 40 ms (TDM40), 100 ms (TDM100) and 300 ms (TDM300). The tracking quality was evaluated by comparing the reference and generated trajectories using cross-correlation coefficient (CCCOEF), time delay, root mean square tracking error, and the amount of overshoot., Results: The control performance in CONT-CLT was the best in all the outcome measures. The overall worst performance was obtained using TDM with the shortest FW (TDM40). There was no significant difference between TDM100 and TDM300, and the quality of tracking in these two conditions was high (CCCOEF ~ 0.95). The results demonstrated that FW duration is indeed an important parameter in TDM, which appears to have an optimal value. Among the tested cases, the FW duration of 100 ms seems to be the best trade-off between the quality of perception and a limited command update rate., Conclusions: This study represents the first systematic evaluation of a TDM-based approach for closing the loop using electrotactile feedback in myoelectric systems. The overall conclusion is that TDM is a feasible and attractive method for closed-loop myocontrol, since it is easy to implement (software-only solution), has limited impact on the performance when using proper FW duration, and might decrease habituation due to burst-like stimulation delivery.

An important reason for the abandonment of commercial actuated hand prostheses by the users is the lack of sensory feedback. Wearable afferent interfaces capable of providing electro- or vibro-tactile stimulation have high potential to restore the missing tactile and/or proprioceptive information to the user. By definition, these devices can elicit single modality (i.e., either vibrotactile or electrotactile) substitute sensations. In a recent research we have presented a novel approach comprising hybrid vibro-electrotactile (HyVE) combined stimulation, in order to provide multimodal sensory feedback. An important advantage of this approach is in the size of the design: the HyVE interface is much more compact than two separated single-modality interfaces, since electro- and vibro-tactile stimulators are placed one on top of the other. The HyVE approach has been previously tested in healthy subjects and has shown to provide a range of hybrid stimuli that could be properly discriminated. However, this approach has never been assessed as a method to provide multi-channel stimuli, i.e., stimuli from a variety of stimulators, mapping information from a multitude of sensors on a prosthesis. In this study, the ability of ten healthy subjects to discriminate stimuli and patterns of stimuli from four different five-channel interfaces applied on their forearms was evaluated. We showed that multiple HyVE units could be used to provide multi-channel sensory information with equivalent performance (∼95 percent for single stimuli and ∼80 percent for pattern) to single modality interfaces (vibro- or electro-tactile) larger in size and with better performance than vibrotactile interfaces (i.e., 73 percent for single stimuli and 69 percent for pattern) with the same size. These results are promising in relation to the current availability of multi-functional prostheses with multiple sensors.

Electro- or vibro-tactile stimulations were used in the past to provide sensory information in many different applications ranging from human manual control to prosthetics. The two modalities were used separately in the past, and we hypothesized that a hybrid vibro-electrotactile (HyVE) stimulation could provide two afferent streams that are independently perceived by a subject, although delivered in parallel and through the same skin location. We conducted psychophysical experiments where healthy subjects were asked to recognize the intensities of electroand vibro-tactile stimuli during hybrid and single modality stimulations. The results demonstrated that the subjects were able to discriminate the features of the two modalities within the hybrid stimulus, and that the cross-modality interaction was limited enough to allow better transmission of discrete information (messages) using hybrid versus singlemodality coding. The percentages of successful recognitions (mean ± standard deviation) for nine messages were 56 ± 11 % and 72 ± 8 % for two hybrid coding schemes, compared to 29 ±7 % for vibrotactile and 44 ± 4 % for electrotactile coding. The HyVE can be therefore an attractivesolution in numerous application for providing sensory feedbackin prostheses and rehabilitation, and it could be used to increase the resolution of a single variable or to simultaneously feedback two different variables.