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Introduction: Improving the robustness of myoelectric control to work over many months without the need for recalibration could reduce prosthesis abandonment. Current approaches rely on post-hoc error detection to verify the certainty of a decoder's prediction using predefined threshold value. Since the decoder is fixed, the performance decline over time is inevitable. Other approaches such as supervised recalibration and unsupervised self- recalibration entail limitations in scaling up and computational resources. The objective of this paper is to study active learning as a scalable, human-in-the- loop framework, to improve the robustness of myoelectric control. Method: Active learning and linear discriminate analysis methods were used to create an iterative learning process, to modify decision boundaries based on changes in the data. We simulated a real-time scenario. We exploited least confidence, smallest margin and entropy reduction sampling strategies in single and batch-mode sample selection. Optimal batch-mode sampling was considered using ranked batch-mode active learning. Results: With only 3.2 min of data carefully selected by the active learner, the decoder outperforms random sampling by 4-5 and -2% for able-bodied and people with limb difference, respectively. We observed active learning strategies to systematically and significantly enhance the decoders adaptation while optimizing the amount of training data on a class-specific basis. Smallest margin and least confidence uncertainty were shown to be the most supreme. Discussion: We introduce for the first time active learning framework for long term adaptation in myoelectric control. This study simulates closed-loop environment in an offliine manner and proposes a pipeline for future real-time deployment. [ABSTRACT FROM AUTHOR]

Introduction: Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. Methods: The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. Results: The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Discussion: Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands. [ABSTRACT FROM AUTHOR]

Introduction: Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by armpostures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. Method: This work focuses on twomajor functional wristmovements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. Result: The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks. [ABSTRACT FROM AUTHOR]

This document is an editorial titled "Horizons in neurorobotics 2022" published in Frontiers in Neurorobotics. It highlights a research topic in neurorobotics and presents five articles that contribute to the field. The articles cover various topics such as explainable AI in neuro-robotics, optimization algorithms for robotic manipulators, deep causal learning for robotic intelligence, control systems for myoelectric prostheses, and advancements in multimodal human-robot interaction. The editorial emphasizes the multidisciplinary nature of neurorobotics and its potential to revolutionize our understanding and capabilities in the field. [Extracted from the article]

This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support VectorMachine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%. [ABSTRACT FROM AUTHOR]

The validation of myoelectric prosthetic control strategies for individuals experiencing upper-limb loss is hindered by the time and cost affiliated with traditional custom-fabricated sockets. Consequently, researchers often rely upon virtual reality or robotic arms to validate novel control strategies, which limits end-user involvement. Prosthetists fabricate diagnostic check sockets to assess and refine socket fit, but these clinical techniques are not readily available to researchers and are not intended to assess functionality for control strategies. Here we present a multi-user, low-cost, transradial, functional-test socket for short-term research use that can be custom-fit and donned rapidly, used in conjunction with various electromyography configurations, and adapted for use with various residual limbs and terminal devices. In this study, participants with upper-limb amputation completed functional tasks in physical and virtual environments both with and without the socket, and they reported on their perceived comfort level over time. The functional-test socket was fabricated prior to participants' arrival, iteratively fitted by the researchers within 10 mins, and donned in under 1 min (excluding electrode placement, which will vary for different use cases). It accommodated multiple individuals and terminal devices and had a total cost of materials under $10 USD. Across all participants, the socket did not significantly impede functional task performance or reduce the electromyography signal-to-noise ratio. The socket was rated as comfortable enough for at least 2 h of use, though it was expectedly perceived as less comfortable than a clinically-prescribed daily-use socket. The development of this multi-user, transradial, functional-test socket constitutes an important step toward increased end-user participation in advanced myoelectric prosthetic research. The socket design has been open-sourced and is available for other researchers. [ABSTRACT FROM AUTHOR]

This article will address the case of active upper-limb prostheses to discuss the importance and the limits of the neuroergonomics approach and human-centered design. Footnotes 1 In this perspective, I argue that the inability to take into account the first-person experience of the prostheses use may afflict the phase of personalization of the prosthesis. Specifically, this new methodology helps investigate upper limb prostheses with haptic feedback as the sensory feedback implies the first-person dimension. [Extracted from the article]

Modern myoelectric prostheses can perform multiple functions (e.g., several grasp types and wrist rotation) but their intuitive control by the user is still an open challenge. It has been recently demonstrated that semi-autonomous control can allow the subjects to operate complex prostheses effectively; however, this approach often requires placing sensors on the user. The present study proposes a system for semi-autonomous control of a myoelectric prosthesis that requires a single depth sensor placed on the dorsal side of the hand. The system automatically pre-shapes the hand (grasp type, size, and wrist rotation) and allows the user to grasp objects of different shapes, sizes and orientations, placed individually or within cluttered scenes. The system "reacts" to the side from which the object is approached, and enables the user to target not only the whole object but also an object part. Another unique aspect of the system is that it relies on online interaction between the user and the prosthesis; the system reacts continuously on the targets that are in its focus, while the user interprets the movement of the prosthesis to adjust aiming. Experimental assessment was conducted in ten able-bodied participants to evaluate the feasibility and the impact of training on prosthesis-user interaction. The subjects used the system to grasp a set of objects individually (Phase I) and in cluttered scenarios (Phase II), while the time to accomplish the task (TAT) was used as the performance metric. In both phases, the TAT improved significantly across blocks. Some targets (objects and/or their parts) were more challenging, requiring thus significantly more time to handle, but all objects and scenes were successfully accomplished by all subjects. The assessment therefore demonstrated that the system is indeed robust and effective, and that the subjects could successfully learn how to aim with the system after a brief training. This is an important step toward the development of a self-contained semi-autonomous system convenient for clinical applications. [ABSTRACT FROM AUTHOR]

We propose, implement, and evaluate a natural human-machine control interface for a variable stiffness transradial hand prosthesis that achieves tele-impedance control through surface electromyography (sEMG) signals. This interface, together with variable stiffness actuation (VSA), enables an amputee to modulate the impedance of the prosthetic limb to properly match the requirements of a task while performing activities of daily living (ADL). Both the desired position and stiffness references are estimated through sEMG signals and used to control the VSA hand prosthesis. In particular, regulation of hand impedance is managed through the impedance measurements of the intact upper arm; this control takes place naturally and automatically as the amputee interacts with the environment, while the position of the hand prosthesis is regulated intentionally by the amputee through the estimated position of the shoulder. The proposed approach is advantageous since the impedance regulation takes place naturally without requiring amputees' attention and diminishing their functional capability. Consequently, the proposed interface is easy to use, does not require long training periods or interferes with the control of intact body segments. This control approach is evaluated through human subject experiments conducted over able volunteers where adequate estimation of references and independent control of position and stiffness are demonstrated. [ABSTRACT FROM AUTHOR]

This study presents a new approach for an sEMG hand prosthesis based on a 3D printed model with a fully embedded computer vision (CV) system in a hybrid version. A modified 5-layer Smaller Visual Geometry Group (VGG) convolutional neural network (CNN), running on a Raspberry Pi 3 microcomputer connected to a webcam, recognizes the shape of daily use objects, and defines the pattern of the prosthetic grasp/gesture among five classes: Palmar Neutral, Palmar Pronated, Tripod Pinch, Key Grasp, and Index Finger Extension. Using the Myoware board and a finite state machine, the user's intention, depicted by a myoelectric signal, starts the process, photographing the object, proceeding to the grasp/gesture classification, and commands the prosthetic motors to execute the movements. Keras software was used as an application programming interface and TensorFlow as numerical computing software. The proposed system obtained 99% accuracy, 97% sensitivity, and 99% specificity, showing that the CV system is a promising technology to assist the definition of the grasp pattern in prosthetic devices. [ABSTRACT FROM AUTHOR]

Poly-articulated hands, actuated by multiple motors and controlled by surface myoelectric technologies, represent the most advanced aids among commercial prostheses. However, simple hook-like body-powered solutions are still preferred for their robustness and control reliability, especially for challenging environments (such as those encountered in manual work or developing countries). This study presents the mechatronic implementation and the usability assessment of the SoftHand Pro-Hybrid, a family of poly-articulated, electrically-actuated, and body-controlled artificial hands, which combines the main advantages of both body-powered and myoelectric systems in a single device. An assessment of the proposed system is performed with individuals with and without limb loss, using as a benchmark the SoftHand Pro, which shares the same soft mechanical architecture, but is controlled using surface electromyographic sensors. Results indicate comparable task performance between the two control methods and suggest the potential of the SoftHand Pro-Hybrid configurations as a viable alternative to myoelectric control, especially in work and demanding environments. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of 12 combinations of three feature selection methods [no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)] and four classification methods [non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)] in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p < 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p < 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses. [ABSTRACT FROM AUTHOR]

A muscular synergy is a theory suggesting that the central nervous system uses few commands to activate a group of muscles to produce a given movement. Here, we investigate how a muscle synergy extracted from a single muscle can be at the origin of different signals which could facilitate the control of modern upper limb myoelectric prostheses with many degrees of freedom. Five pairs of surface electrodes were positioned across the biceps of 12 normal subjects and electromyographic (EMG) signals were collected while their upper limbs were in eight different static postures. Those signals were used to move, within a virtual cube, a small red sphere toward different targets. With three muscular synergies extracted from the five EMG signals, a classifier was trained to identify which synergy pattern was associated with a given static posture. Later, when a posture was recognized, the result was a displacement of a red sphere toward a corner of a virtual cube presented on a computer screen. The axes of the cube were assigned to the shoulder, elbow and wrist joint while each of its the corners was associated with a static posture. The goal for subjects was to reach, one at a time, the four targets positioned at different locations and heights in the virtual cube with different sequences of postures. The results of 12 normal subjects indicate that with the muscular synergies of the biceps brachii, it was possible, but not easy for an untrained person, to reach a target on each trial. Thus, as a proof of concept, we show that features of the biceps muscular synergy have the potential to facilitate the control of upper limb myoelectric prostheses. To our knowledge, this has never been shown before.

We developed an intuitively operational shoulder disarticulation prosthesis system that can be used without long-term training. The developed system consisted of four degrees of freedom joints, as well as a user adapting control system based on a machine learning technique and surface electromyogram (EMG) of the trunk. We measured the surface EMG of the trunk of healthy subjects at multiple points and analyzed through principal component analysis to identify the proper EMG measurement portion of the trunk, which was determined to be distributed in the chest and back. Additionally, evaluation experiments demonstrated the capability of four healthy subjects to grasp and move objects in the horizontal as well as the vertical directions, using our developed system controlled via the EMG of the chest and back. Moreover, we also quantitatively confirmed the ability of a bilateral shoulder disarticulation amputee to complete the evaluation experiment similar to healthy subjects. [ABSTRACT FROM AUTHOR]

To physically interact with a rich variety of environments and to match situation-dependent requirements, humans adapt both the force and stiffness of their limbs. Reflecting this behavior in prostheses may promote a more natural and intuitive control and, consequently, improve prostheses acceptance in everyday life. This pilot study proposes a method to control a prosthetic robot hand and its impedance, and explores the utility of variable stiffness when performing activities of daily living and physical social interactions. The proposed method is capable of a simultaneous and proportional decoding of position and stiffness intentions from two surface electro-myographic sensors placed over a pair of antagonistic muscles. The feasibility of our approach is validated and compared to existing control modalities in a preliminary study involving one prosthesis user. The algorithm is implemented in a soft under-actuated prosthetic hand (SoftHand Pro). Then, we evaluate the usability of the proposed approach while executing a variety of tasks. Among these tasks, the user interacts with other 12 able-bodied subjects, whose experiences were also assessed. Several statistically significant aspects from the System Usability Scale indicate user's preference of variable stiffness control over low or high constant stiffness due to its reactivity and adaptability. Feedback reported by able-bodied subjects reveal a general tendency to favor soft interaction, i.e., low stiffness, which is perceived more human-like and comfortable. These combined results suggest the use of variable stiffness as a viable compromise between firm control and safe interaction which is worth investigating further. [ABSTRACT FROM AUTHOR]

A muscular synergy is a theory suggesting that the central nervous system uses few commands to activate a group of muscles to produce a given movement. Here, we investigate how a muscle synergy extracted from a single muscle can be at the origin of different signals which could facilitate the control of modern upper limb myoelectric prostheses with many degrees of freedom. Five pairs of surface electrodes were positioned across the biceps of 12 normal subjects and electromyographic (EMG) signals were collected while their upper limbs were in eight different static postures. Those signals were used to move, within a virtual cube, a small red sphere toward different targets. With three muscular synergies extracted from the five EMG signals, a classifier was trained to identify which synergy pattern was associated with a given static posture. Later, when a posture was recognized, the result was a displacement of a red sphere toward a corner of a virtual cube presented on a computer screen. The axes of the cube were assigned to the shoulder, elbow and wrist joint while each of its the corners was associated with a static posture. The goal for subjects was to reach, one at a time, the four targets positioned at different locations and heights in the virtual cube with different sequences of postures. The results of 12 normal subjects indicate that with the muscular synergies of the biceps brachii, it was possible, but not easy for an untrained person, to reach a target on each trial. Thus, as a proof of concept, we show that features of the biceps muscular synergy have the potential to facilitate the control of upper limb myoelectric prostheses. To our knowledge, this has never been shown before. [ABSTRACT FROM AUTHOR]

In this study, a one-degree-of-freedom myoelectric prosthesis system was proposed using a Parent Wireless Assistive Interface (PWAI) that allowed an external assistant (e. g., the parent of the user) to immediately adjust the parameters of the prosthetic hand controller. In the PWAI, the myoelectric potential of use of the upper limb was plotted on an external terminal in real time. Simultaneously, the assistant adjusted the parameters of the prosthetic hand control device and manually manipulated the prosthetic hand. With these functions, children that have difficulty verbally communicating could obtain properly adjusted prosthetic hands. In addition, non-experts could easily adjust and manually manipulate the prosthesis; therefore, training for the prosthetic hands could be performed at home. Two types of hand motion discrimination methods were constructed in this study of the myoelectric control system: (1) a threshold control based on the myoelectric potential amplitude information and (2) a pattern recognition of the frequency domain features. In an evaluation test of the prosthesis threshold control system, child subjects achieved discrimination rates as high as 89%, compared with 96% achieved by adult subjects. Furthermore, the high discrimination rate was maintained by sequentially updating the threshold value. In addition, a discrimination rate of 82% on average was obtained by recognizing three motions using the pattern recognition method. [ABSTRACT FROM AUTHOR]

Sensorimotor coordination mechanism, multimodality, human-robot interaction, bio-inspired models, coadaptation in interaction Keywords: sensorimotor coordination mechanism; multimodality; human-robot interaction; bio-inspired models; coadaptation in interaction EN sensorimotor coordination mechanism multimodality human-robot interaction bio-inspired models coadaptation in interaction N.PAG N.PAG 3 04/26/21 20210415 NES 210415 With the widespread application of intelligent robots and all kinds of sensors in our lives, human-robot interaction has become a significant research direction. The goal of human-robot interaction research is to make robots capable of operating in human-centered spaces to enhance work efficiency, introduce flexibility and adaptability in solutions, and improve the quality of human life. [Extracted from the article]

Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients. [ABSTRACT FROM AUTHOR]

A key factor for reliable object manipulation is the tactile information provided by the skin of our hands. As this sensory information is so essential in our daily life it should also be provided during teleoperation of robotic devices or in the control of myoelectric prostheses. It is well-known that feeding back the tactile information to the user can lead to a more natural and intuitive control of robotic devices. However, in some applications it is difficult to use the hands as natural feedback channels since they may already be overloaded with other tasks or, e.g., in case of hand prostheses not accessible at all. Many alternatives for tactile feedback to the human hand have already been investigated. In particular, one approach shows that humans can integrate uni-directional (normal) force feedback at the toe into their sensorimotor-control loop. Extending this work, we investigate the human's capability to discriminate spatial forces at the bare front side of their toe. A state-of-the-art haptic feedback device was used to apply forces with three different amplitudes--2N, 5N, and 8N--to subjects' right big toes. During the experiments, different force stimuli were presented, i.e., direction of the applied force was changed, such that tangential components occured. In total the four directions up (distal), down (proximal), left (medial), and right (lateral) were tested. The proportion of the tangential force was varied corresponding to a directional change of 5° to 25° with respect to the normal force. Given these force stimuli, the subjects' task was to identify the direction of the force change. We found the amplitude of the force as well as the proportion of tangential forces to have a significant influence on the success rate. Furthermore, the direction right showed a significantly different successrate from all other directions. The stimuli with a force amplitude of 8N achieved success rates over 89% in all directions. The results of the user study provide evidence that the subjects were able to discriminate spatial forces at their toe within defined force amplitudes and tangential proportion. [ABSTRACT FROM AUTHOR]

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands. [ABSTRACT FROM AUTHOR]

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager–Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.

Missing an upper limb dramatically impairs daily-life activities. Efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective article, we suggest that one relevant factor determining the relatively small clinical impact of myocontrol algorithms for upper limb prostheses is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively support this argument with representative data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reaching relatively high classification accuracy offline, their clinical scores varied greatly and were not strongly predicted by classification accuracy. We therefore support the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field. [ABSTRACT FROM AUTHOR]

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated. [ABSTRACT FROM AUTHOR]

Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/ autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions. [ABSTRACT FROM AUTHOR]

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses. [ABSTRACT FROM AUTHOR]

Users of myoelectric prostheses can often find them difficult to control. This can lead to passive-use of the device or total rejection, which can have detrimental effects on the contralateral limb due to overuse. Current clinically available prostheses are “open loop” systems, and although considerable effort has been focused on developing biofeedback to “close the loop,” there is evidence from laboratory-based studies that other factors, notably improving predictability of response, may be as, if not more, important. Interestingly, despite a large volume of research aimed at improving myoelectric prostheses, it is not currently known which aspect of clinically available systems has the greatest impact on overall functionality and everyday usage. A protocol has, therefore, been designed to assess electromyographic (EMG) skill of the user and predictability of the prosthesis response as significant parts of the control chain, and to relate these to functionality and everyday usage. Here, we present the protocol and results from early pilot work. A set of experiments has been developed. First, to characterize user skill in generating the required level of EMG signal, as well as the speed with which users are able to make the decision to activate the appropriate muscles. Second, to measure unpredictability introduced at the skin–electrode interface, in order to understand the effects of the socket-mounted electrode fit under different loads on the variability of time taken for the prosthetic hand to respond. To evaluate prosthesis user functionality, four different outcome measures are assessed. Using a simple upper limb functional task prosthesis users are assessed for (1) success of task completion, (2) task duration, (3) quality of movement, and (4) gaze behavior. To evaluate everyday usage away from the clinic, the symmetricity of their real-world arm use is assessed using activity monitoring. These methods will later be used to assess a prosthesis user cohort to establish the relative contribution of each control factor to the individual measures of functionality and everyday usage (using multiple regression models). The results will support future researchers, designers, and clinicians in concentrating their efforts on the area that will have the greatest impact on improving prosthesis use. [ABSTRACT FROM AUTHOR]

In recent years, the development of myoelectric control for robotic lower-limb prostheses makes it possible for amputee users to volitionally control prosthetic joints. However, the human-centered control loop is not closed due to the lack of sufficient feedback of prosthetic joint movement, and it may result in poor control performance. In this research, we propose a vibrotactile stimulation system to provide the feedback of ankle joint position, and validate the necessity of combining it with volitional myoelectric control to achieve improved control performance. The stimulation system is wearable and consists of six vibrators. Three of the vibrators are placed on the anterior side of the thigh and the other three on the posterior side of the thigh. To explore the potential of applying the proposed vibrotactile feedback system for prosthetic ankle control, eight able-bodied subjects and two transtibial amputee subjects (TT1 and TT2) were recruited in this research, and several experiments were designed to investigate subjects’ sensitivities to discrete and continuous vibration stimulations applied on the thigh. Then, we proposed a stimulation controller to produce different stimulation patterns according to current ankle angle. Amputee subjects were asked to control a virtual ankle displayed on the computer screen to reach different target ankle angles with a myoelectric controller, and control performances under different feedback conditions were compared. Experimental results indicated that subjects were more sensitive to stimulation position changes (identification accuracies were 96.39 ± 0.86, 91.11, and 93.89% for able-bodied subjects, TT1, and TT2, respectively) than stimulation amplitude changes (identification accuracies were 89.89 ± 2.40, 87.04, and 85.19% for able-bodied subjects, TT1, and TT2, respectively). Response times of able-bodied subjects, TT1, and TT2 to stimulation pattern changes were 0.47 ± 0.02 s, 0.53 s, and 0.48 s, respectively. Furthermore, for both TT1 and TT2, the absolute error of virtual ankle control reduced by about 50% with the addition of vibrotactile feedback. These results suggest that it is promising to apply the vibrotactile feedback system for the control of robotic transtibial prostheses. [ABSTRACT FROM AUTHOR]

This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability ( p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Lu, Fan, Wang, Zhang, Li and Tao.)

Stable myoelectric control of hand prostheses remains an open problem. The only successful human- machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regularly retrain a new model from scratch. We hereby propose a non-linear incremental learning method in which occasional updates with a modest amount of novel training data allow continual adaptation to the changes in the signals. In particular, Incremental Ridge Regression and an approximation of the Gaussian Kernel known as Random Fourier Features are combined to predict finger forces from myoelectric signals, both finger-by-finger and grouped in grasping patterns. We show that the approach is effective and practically applicable to this problem by first analyzing its performance while predicting single-finger forces. Surface electromyography and finger forces were collected from 10 intact subjects during four sessions spread over two different days; the results of the analysis show that small incremental updates are indeed effective to maintain a stable level of performance. Subsequently, we employed the same method on-line to teleoperate a humanoid robotic arm equipped with a state-of-the-art commercial prosthetic hand. The subject could reliably grasp, carry and release everyday-life objects, enforcing stable grasping irrespective of the signal changes, hand/arm movements and wrist pronation and supination. [ABSTRACT FROM AUTHOR]

Electromyogram (EMG) contains rich information for motion decoding. As one of its major applications, EMG-pattern recognition (PR)-based control of prostheses has been proposed and investigated in the field of rehabilitation robotics for decades. These prostheses can offer a higher level of dexterity compared to the commercially available ones. However, limited progress has been made toward clinical application of EMG-PR-based prostheses, due to their unsatisfactory robustness against various interferences during daily use. These interferences may lead to misclassifications of motion intentions, which damage the control performance of EMG-PR-based prostheses. A number of studies have applied methods that undergo a postprocessing stage to determine the current motion outputs, based on previous outputs or other information, which have proved effective in reducing erroneous outputs. In this study, we proposed a postprocessing strategy that locks the outputs during the constant contraction to block out occasional misclassifications, upon detecting the motion onset using a threshold. The strategy was investigated using three different motion onset detectors, namely mean absolute value, Teager-Kaiser energy operator, or mechanomyogram (MMG). Our results indicate that the proposed strategy could suppress erroneous outputs, during rest and constant contractions in particular. In addition, with MMG as the motion onset detector, the strategy was found to produce the most significant improvement in the performance, reducing the total errors up to around 50% (from 22.9 to 11.5%) in comparison to the original classification output in the online test, and it is the most robust against threshold value changes. We speculate that motion onset detectors that are both smooth and responsive would further enhance the efficacy of the proposed postprocessing strategy, which would facilitate the clinical application of EMG-PR-based prosthetic control.

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.