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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).)

Background: Lower limb amputation does not affect only physical and psychological functioning but the use of a prosthetic device can also lead to increased cognitive demands. Measuring cognitive load objectively is challenging, and therefore, most studies use questionnaires that are easy to apply but can suffer from subjective bias. Motivated by this, the present study investigated whether a mobile eye tracker can be used to objectively measure cognitive load by monitoring gaze behavior during a set of motor tasks., Methods: Five prosthetic users and eight able-bodied controls participated in this study. Eye tracking data and kinematics were recorded during a set of motor tasks (level ground walking, walking on uneven terrain, obstacle avoidance, stairs up and ramp down, as well as ramp up and stairs down) while the participants were asked to focus their gaze on a visual target for as long as possible. Target fixation times and increase in pupil diameters were determined and correlated to subjective ratings of cognitive load., Findings: Overall, target fixation time and pupil diameter showed strong negative and positive correlations, respectively, to the subjective rating of cognitive load in the able-bodied controls (-0.75 and 0.80, respectively). However, the individual correlation strength, and in some cases, even the sign, was different across participants. A similar trend could be observed in prosthetic users., Interpretation: The results of this study showed that a mobile eye tracker may be used to estimate cognitive load in prosthesis users during locomotor tasks. This paves the way to establish a new approach to assessing cognitive load, which is objective and yet practical and simple to administer. Nevertheless, future studies should corroborate these results by comparing them to other objective measures as well as focus on translating the proposed approach outside of a laboratory., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sabina Manz reports that prosthetic equipment to conduct experiments was provided by Ottobock SE & Co KGaA. Thomas Schmalz, Michael Ernst, Thomas Maximilian Köhler, and Jose Gonzalez-Vargas are employed by Ottobock SE & Co KGaA. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

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.

Background: Closing the control loop in myoelectric prostheses by providing artificial somatosensory feedback is recognized as an important goal. However, designing a feedback interface that is effective in realistic conditions is still a challenge. Namely, in some situations, feedback can be redundant, as the information it provides can be readily obtained through hearing or vision (e.g., grasping force estimated from the deformation of a compliant object). EMG feedback is a non-invasive method wherein the tactile stimulation conveys to the user the level of their own myoelectric signal, hence a measurement intrinsic to the interface, which cannot be accessed incidentally., Methods: The present study investigated the efficacy of EMG feedback in prosthesis force control when 10 able-bodied participants and a person with transradial amputation used a myoelectric prosthesis to grasp compliant objects of different stiffness values. The performance with feedback was compared to that achieved when the participants relied solely on incidental cues., Results: The main outcome measures were the task success rate and completion time. EMG feedback resulted in significantly higher success rates regardless of pin stiffness, indicating that the feedback enhanced the accuracy of force application despite the abundance of incidental cues. Contrary to expectations, there was no difference in the completion time between the two feedback conditions. Additionally, the data revealed that the participants could produce smoother control signals when they received EMG feedback as well as more consistent commands across trials, signifying better control of the system by the participants., Conclusions: The results presented in this study further support the efficacy of EMG feedback when closing the prosthesis control loop by demonstrating its benefits in particularly challenging conditions which maximized the utility of intrinsic feedback sources., (© 2023. BioMed Central Ltd., part of Springer Nature.)

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

Background: Biomechanical gait analyses are typically performed in laboratory settings, and are associated with limitations due to space, marker placement, and tasks that are not representative of the real-world usage of lower limb prostheses. Therefore, the purpose of this study was to investigate the possibility of accurately measuring gait parameters using embedded sensors in a microprocessor-controlled knee joint., Methods: Ten participants were recruited for this study and equipped with a Genium X3 prosthetic knee joint. They performed level walking, stair/ramp descent, and ascent. During these tasks, kinematics and kinetics (sagittal knee and thigh segment angle, and knee moment) were recorded using an optical motion capture system and force plates (gold standard), as well as the prosthesis-embedded sensors. Root mean square errors, relative errors, correlation coefficients, and discrete outcome variables of clinical relevance were calculated and compared between the gold standard and the embedded sensors., Findings: The average root mean square errors were found to be 0.6°, 5.3°, and 0.08 Nm/kg, for the knee angle, thigh angle, and knee moment, respectively. The average relative errors were 0.75% for the knee angle, 11.67% for the thigh angle, and 9.66%, for the knee moment. The discrete outcome variables showed small but significant differences between the two measurement systems for a number of tasks (higher differences only at the thigh)., Interpretation: The findings highlight the potential of prosthesis-embedded sensors to accurately measure gait parameters across a wide range of tasks. This paves the way for assessing prosthesis performance in realistic environments outside the lab., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sabina Manz reports a relationship with Ottobock SE & Co KGaA that includes: employment. Dirk Seifert reports a relationship with Ottobock Healthcare Products GmbH that includes: employment. Bjoern Altenburg reports a relationship with Ottobock SE & Co KGaA that includes: employment. Thomas Schmalz reports a relationship with Ottobock SE & Co KGaA that includes: employment. Jose Gonzalez-Vargas reports a relationship with Ottobock SE & Co KGaA that includes: employment., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

State-of-the-art myoelectric hand prostheses provide multi-functional control but lack somatosensory feedback. To accommodate the full functionality of a dexterous prosthesis, the artificial sensory feedback needs to convey several degrees of freedom (DoF) simultaneously. However, this is a challenge with current methods as they are characterized by a low information bandwidth. In this study, we leverage the flexibility of a recently developed system for simultaneous electrotactile stimulation and electromyography (EMG) recording to present the first solution for closed-loop myoelectric control of a multifunctional prosthesis with full-state anatomically congruent electrotactile feedback. The novel feedback scheme (coupled encoding) conveyed proprioceptive (hand aperture, wrist rotation) and exteroceptive information (grasping force). The coupled encoding was compared to the conventional approach (sectorized encoding) and incidental feedback in 10 non-disabled and one amputee participant who used the system to perform a functional task. The results showed that both feedback approaches increased the accuracy of position control compared to incidental feedback. However, the feedback increased completion time, and it did not significantly improve grasping force control. Importantly, the performance of the coupled feedback was not significantly different compared to the conventional scheme, despite the latter being easier to learn during training. Overall, the results indicate that the developed feedback can improve prosthesis control across multiple DoFs but they also highlight the subjects' ability to exploit minimal incidental information. Importantly, the current setup is the first to convey three feedback variables simultaneously using electrotactile stimulation while providing multi-DoF myoelectric control with all hardware components mounted on the same forearm.

Reliable force control is especially important when using myoelectric upper-limb prostheses as the force defines whether an object will be firmly grasped, damaged, or dropped. It is known from human motor control that the grasping of non-disabled subjects is based on a combination of anticipation and feedback correction. Inspired by this insight, the present study proposes a novel approach to provide artificial sensory feedback to the user of a myoelectric prosthesis using vibrotactile stimulation to facilitate both predictive and corrective processes characteristic of grasping in non-disabled people. Specifically, the level of EMG was conveyed to the subjects while closing the prosthesis (predictive strategy), whereas the actual grasping force was transmitted when the prosthesis closed (corrective strategy). To investigate if this combined EMG and force feedback is indeed an effective method to explicitly close the control loop, 16 non-disabled and 3 transradial amputee subjects performed a set of functional tasks, inspired by the "Box and Block" test, with six target force levels, in three conditions: no feedback, only EMG feedback, and combined feedback. The highest overall performance in non-disabled subjects was obtained with combined feedback (79.6±9.9%), whereas the lowest was achieved with no feedback (53±11.5%). The combined feedback, however, increased the task completion time compared to the other two conditions. A similar trend was obtained also in three amputee subjects. The results, therefore, indicate that the feedback inspired by human motor control is indeed an effective approach to improve prosthesis grasping in realistic conditions when other sources of feedback (vision and audition) are not blocked.

Background: The development of bionic legs has seen substantial improvements in the past years but people with lower-limb amputation still suffer from impairments in mobility (e.g., altered balance and gait control) due to significant limitations of the contemporary prostheses. Approaching the problem from a human-centered perspective by focusing on user-specific needs can allow identifying critical improvements that can increase the quality of life. While there are several reviews of user needs regarding upper limb prostheses, a comprehensive summary of such needs for those affected by lower limb loss does not exist., Methods: We have conducted a systematic review of the literature to extract important needs of the users of lower-limb prostheses. The review included 56 articles in which a need (desire, wish) was reported explicitly by the recruited people with lower limb amputation (N = 8149)., Results: An exhaustive list of user needs was collected and subdivided into functional, psychological, cognitive, ergonomics, and other domain. Where appropriate, we have also briefly discussed the developments in prosthetic devices that are related to or could have an impact on those needs. In summary, the users would like to lead an independent life and reintegrate into society by coming back to work and participating in social and leisure activities. Efficient, versatile, and stable gait, but also support to other activities (e.g., sit to stand), contribute to safety and confidence, while appearance and comfort are important for the body image. However, the relation between specific needs, objective measures of performance, and overall satisfaction and quality of life is still an open question., Conclusions: Identifying user needs is a critical step for the development of new generation lower limb prostheses that aim to improve the quality of life of their users. However, this is not a simple task, as the needs interact with each other and depend on multiple factors (e.g., mobility level, age, gender), while evolving in time with the use of the device. Hence, novel assessment methods are required that can evaluate the impact of the system from a holistic perspective, capturing objective outcomes but also overall user experience and satisfaction in the relevant environment (daily life)., (© 2022. 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).)

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. Supplemental sensory feedback for myoelectric prostheses can provide both psychosocial and functional benefits during prosthesis control. However, the impact of feedback depends on multiple factors and there is insufficient understanding about the fundamental role of such feedback in prosthesis use. The framework of human motor control enables us to systematically investigate the user-prosthesis control loop. In this study, we explore how different task objectives such as speed and accuracy shape the control policy developed by participants in a prosthesis force-matching task. Approach. Participants were randomly assigned to two groups that both used identical electromyography control interface and prosthesis force feedback, through vibrotactile stimulation, to perform a prosthesis force-matching task. However, the groups received different task objectives specifying speed and accuracy demands. We then investigated the control policies developed by the participants. To this end, we not only evaluated how successful or fast participants were but also analyzed the behavioral strategies adopted by the participants to obtain such performance gains. Main results. First, we observed that participants successfully integrated supplemental prosthesis force feedback to develop both feedforward and feedback control policies, as demanded by the task objectives. We then observed that participants who first developed a (slow) feedback policy were quickly able to adapt their policy to more stringent speed demands, by switching to a combined feedforward-feedback control strategy. However, the participants who first developed a (fast) feedforward policy were not able to change their control policy and adjust to greater accuracy demands. Significance. Overall, the results signify how the framework of human motor control can be applied to study the role of feedback in user-prosthesis interaction. The results also reveal the utility of training prosthesis users to integrate supplemental feedback into their state estimation by designing training protocols that encourage the development of combined feedforward and feedback policy., (© 2021 IOP Publishing Ltd.)

Objective. The nervous system subconsciously estimates the state of the body as a weighted average of the information from various sensory sources, where the weights reflect the perceived reliability of each source. Loss of motor functions can be partially compensated using assistive systems (e.g. prostheses), which may also restore somatosensory feedback through tactile stimulation. Whether such artificial feedback is integrated in the neural state estimation process is not known. Approach. In this study, able-bodied subjects performed a grasp force matching task with supplementary non-invasive electrotactile stimulation with a frequency proportional to grasp force magnitude. Before the task, a brief training session taught the subjects to associate the sensation of electrotactile stimulation with the generated grasp force. In some trials, the force-frequency mapping was biased to introduce an unnoticeable mismatch between natural and electrotactile force feedback, thereby provoking the subject to subconsciously estimate the force as a compromise between the two sources of information. Main results. The outcome of this compromise revealed the weights assigned to each feedback type. The grasp forces were significantly affected by the biased mappings, as indicated by the average estimated relative weights (electrotactile: 0.69 ± 0.29; natural: 0.31 ± 0.29). Across subjects, this weight was correlatedr2=0.75) with the improvement in force matching precision when adding the unbiased electrotactile feedback to the natural force feedback, as predicted by maximum likelihood estimation. This shows that even after minimal training the nervous system adopts electrotactile stimulation as a highly reliable source of information that can improve the precision in the estimation of the grip force. Significance. This result has important implications for the restoration of sensory feedback in upper limb prostheses as it indicates that even non-invasive stimulation can be integrated naturally (i.e. subconsciously and effectively) in the motor controlloop., (© 2021 IOP Publishing Ltd.)

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.)

Objective: This study aims at investigating the functional performance of a novel prosthesis control scheme integrating an inductive tongue interface and myoelectric control. The tongue interface allowed direct selection of the desired grasp while myoelectric signals were used to open and close the robotic hand., Methods: The novel method was compared to a conventional sequential on/off myoelectric control scheme using functional tasks defined by Assistive Hand Assessment protocol. Ten able-bodied participants were fitted with the SmartHand on their left forearm. They used both the conventional myoelectric control and the Tongue and Myoelectric Hybrid interface (TMH) to accomplish two activities of daily living (i.e., preparing a sandwich and gift wrapping). Sessions were video recorded and the outcome measure was the completion time for the subtasks as well as the full tasks., Results: The sandwich task was completed significantly faster, with 19% decrease in the completion time, using the TMH when compared to the conventional sequential on/off myoelectric control scheme (p < 0.05)., Conclusion: The results indicate that the TMH control scheme facilitates the active use of the prosthetic device by simplifying grasp selection, leading thereby to faster completion of challenging and relevant tasks involving bimanual activities.

Background: Despite important advancements in control and mechatronics of myoelectric prostheses, the communication between the user and his/her bionic limb is still unidirectional, as these systems do not provide somatosensory feedback. Electrotactile stimulation is an attractive technology to close the control loop since it allows flexible modulation of multiple parameters and compact interface design via multi-pad electrodes. However, the stimulation interferes with the recording of myoelectric signals and this can be detrimental to control., Methods: We present a novel compact solution for simultaneous recording and stimulation through dynamic blanking of stimulation artefacts. To test the system, a feedback coding scheme communicating wrist rotation and hand aperture was developed specifically to stress the myoelectric control while still providing meaningful information to the subjects. Ten subjects participated in an experiment, where the quality of closed-loop myoelectric control was assessed by controlling a cursor in a two degrees of freedom target-reaching task. The benchmark performance with visual feedback was compared to that achieved by combining visual feedback and electrotactile stimulation as well as by using electrotactile feedback only., Results: There was no significant difference in performance between visual and combined feedback condition with regards to successfully reached targets, time to reach a target, path efficiency and the number of overshoots. Therefore, the quality of myoelectric control was preserved in spite of the stimulation. As expected, the tactile condition was significantly poorer in completion rate (100/4% and 78/25% for combined and tactile condition, respectively) and time to reach a target (9/2 s and 13/4 s for combined and tactile condition, respectively). However, the performance in the tactile condition was still good, with no significant difference in path efficiency (38/8%) and the number of overshoots (0.5/0.4 overshoots), indicating that the stimulation was meaningful for the subjects and useful for closed-loop control., Conclusions: Overall, the results demonstrated that the developed system can provide robust closed-loop control using electrotactile stimulation. The system supports different encoding schemes and allows placing the recording and stimulation electrodes next to each other. This is an important step towards an integrated solution where the developed unit will be embedded into a prosthetic socket.

Background: Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training., Methods: In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback., Results: The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7)., Conclusion: The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.

Objective: Artificial proprioceptive feedback from a myoelectric prosthesis is an important aspect in enhancing embodiment and user satisfaction, possibly lowering the demand for visual attention while controlling a prosthesis in everyday tasks. Contemporary myoelectric prostheses are advanced mechatronic systems with multiple degrees of freedom, and therefore, to communicate the prosthesis state, the feedback interface needs to transmit several variables simultaneously. In the present study, two different configurations for conveying proprioceptive information of wrist rotation and hand aperture through multichannel electrotactile stimulation were developed and evaluated during online myoelectric control., Approach: Myoelectric recordings were acquired from the dominant forearm and electrotactile stimulation was delivered on the non-dominant forearm using a compact interface. The first feedback configuration, which was based on spatial coding, transmitted the information using a moving tactile stimulus, whereas the second, amplitude-based configuration conveyed the position via sensation intensity. Thirteen able-bodied subjects used pattern classification-based myoelectric control with both feedback configurations to accomplish a target-reaching task., Main Results: High task performance (completion rate > 90%) was observed for both configurations, with no significant difference in completion rate, time to reach the target, distance error and path efficiency, respectively., Significance: Overall, the results demonstrated that both feedback configurations allowed subjects to perceive and interpret two feedback variables delivered simultaneously, despite using a compact stimulation interface. This is an encouraging result for the prospect of communicating the full state of a multifunctional hand prosthesis.

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).

In the case of a hand amputation, the affected person can use a myoelectric prosthesis to substitute the missing limb and regain motor functions. Unfortunately, commercial methods for myoelectric control, although robust and simple, are unintuitive and cognitively taxing when applied to an advanced multi-functional prosthesis. The state-of-the-art methods developed in academia are based on machine learning and therefore require long training and suffer from a lack of robustness. This work presents a novel closed-loop multi-level amplitude controller (CMAC), which aims at overcoming these drawbacks. The CMAC implements three degrees-of-freedom (DoF) control by thresholding the muscle contraction intensity during wrist flexion and extension movements. Unique features of the controller are the vibrotactile feedback that communicates the state of the controller to the user and a scheme for proportional control. These components allow exploiting the full dexterity of the prosthesis using a simple two-channel myoelectric interface. The CMAC was compared to a commonly implemented pattern-recognition method (linear discriminant analysis - LDA) using clinically relevant tests in 12 able-bodied and 2 amputee subjects. The experimental assessment demonstrated that CMAC was similarly fast as LDA in dexterous tests (clothespin and cube manipulation), while it was somewhat slower than LDA during a simple, single DoF task (box and blocks). In addition, in all the tasks, LDA and CMAC resulted in a similarly low error rate. On the other hand, to an amputee that could not generate six distinguishable classes using LDA, the CMAC still enabled the control of all the prosthesis DoFs. Importantly, the overall setup and training time in CMAC were significantly lower compared to LDA. In conclusion, the novel method is convenient for clinical applications, and allows substantially higher control dexterity compared to what can be normally achieved using conventional two channel EMG. Therefore, CMAC provides performance comparable to advanced machine-learning algorithms and the robustness and ease of use that is characteristic for the simple two-channel myoelectric interface.

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: The loss of a hand is a traumatic experience that substantially compromises an individual's capability to interact with his environment. The myoelectric prostheses are state-of-the-art (SoA) functional replacements for the lost limbs. Their overall mechanical design and dexterity have improved over the last few decades, but the users have not been able to fully exploit these advances because of the lack of effective and intuitive control. Bimanual tasks are particularly challenging for an amputee since prosthesis control needs to be coordinated with the movement of the sound limb. So far, the bimanual activities have been often neglected by the prosthetic research community., Methods: We present a novel method to prosthesis control, which uses a semi-autonomous approach in order to simplify bimanual interactions. The approach supplements the commercial SoA two-channel myoelectric control with two additional sensors. Two inertial measurement units were attached to the prosthesis and the sound hand to detect the movement of both limbs. Once a bimanual interaction is detected, the system mimics the coordination strategies of able-bodied subjects to automatically adjust the prosthesis wrist rotation (pronation, supination) and grip type (lateral, palmar) to assist the sound hand during a bimanual task. The system has been evaluated in eight able-bodied subjects performing functional uni- and bi-manual tasks using the novel method and SoA two-channel myocontrol. The outcome measures were time to accomplish the task, semi-autonomous system misclassification rate, subjective rating of intuitiveness, and perceived workload (NASA TLX)., Results: The results demonstrated that the novel control interface substantially outperformed the SoA myoelectric control. While using the semi-autonomous control the time to accomplish the task and the perceived workload decreased for 25 and 27%, respectively, while the subjects rated the system as more intuitive then SoA myocontrol., Conclusions: The novel system uses minimal additional hardware (two inertial sensors) and simple processing and it is therefore convenient for practical implementation. By using the proposed control scheme, the prosthesis assists the user's sound hand in performing bimanual interactions while decreasing cognitive burden.

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.

Objective: Dexterous upper-limb prostheses are available today to restore grasping, but an effective and reliable feed-forward control is still missing. The aim of this work was to improve the robustness and reliability of myoelectric control by using context information from sensors embedded within the prosthesis., Approach: We developed a context-driven myoelectric control scheme (cxMYO) that incorporates the inference of context information from proprioception (inertial measurement unit) and exteroception (force and grip aperture) sensors to modulate the outputs of myoelectric control. Further, a realistic evaluation of the cxMYO was performed online in able-bodied subjects using three functional tasks, during which the cxMYO was compared to a purely machine-learning-based myoelectric control (MYO)., Main Results: The results demonstrated that utilizing context information decreased the number of unwanted commands, improving the performance (success rate and dropped objects) in all three functional tasks. Specifically, the median number of objects dropped per round with cxMYO was zero in all three tasks and a significant increase in the number of successful transfers was seen in two out of three functional tasks. Additionally, the subjects reported better user experience., Significance: This is the first online evaluation of a method integrating information from multiple on-board prosthesis sensors to modulate the output of a machine-learning-based myoelectric controller. The proposed scheme is general and presents a simple, non-invasive and cost-effective approach for improving the robustness of myoelectric control.

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.

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: Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control., Approach: We developed a novel context- and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semi-automatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living., Main Results: The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training., Significance: The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees.

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.

Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

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.

In closed-loop control of grasping by hand prostheses, the feedback information sent to the user is usually the actual controlled variable, i.e., the grasp force. Although this choice is intuitive and logical, the force production is only the last step in the process of grasping. Therefore, this study evaluated the performance in controlling grasp strength using a hand prosthesis operated through a complete grasping sequence while varying the feedback variables (e.g., closing velocity, grasping force), which were provided to the user visually or through vibrotactile stimulation. The experiments were conducted on 13 volunteers who controlled the Otto Bock Sensor Hand Speed prosthesis. Results showed that vibrotactile patterns were able to replace the visual feedback. Interestingly, the experiments demonstrated that direct force feedback was not essential for the control of grasping force. The subjects were indeed able to control the grip strength, predictively, by estimating the grasping force from the prosthesis velocity of closing. Therefore, grasping without explicit force feedback is not completely blind, contrary to what is usually assumed. In our study we analyzed grasping with a specific prosthetic device, but the outcomes are also applicable for other devices, with one or more degrees-of-freedom. The necessary condition is that the electromyography (EMG) signal directly and proportionally controls the velocity/grasp force of the hand, which is a common approach among EMG controlled prosthetic devices. The results provide important indications on the design of closed-loop EMG controlled prosthetic systems.

Background: Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands., Methods: The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances., Results: The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only)., Conclusions: The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.

A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1].