Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove

Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements. We first explored this question in healthy non-amputee individuals where the ground-truth kinematics could be readily determined using motion capture. Kinematic data showed that mimic training fails to account for biomechanical coupling and temporal changes in hand posture. Additionally, mirror training exhibited significantly higher accuracy and precision in labeling hand kinematics. These findings suggest that the mirror training approach generates a more faithful, albeit more complex, dataset. Accordingly, mirror training resulted in significantly better offline regression performance when using a large amount of training data and a non-linear neural network. Next, we explored these different training paradigms online, with a cohort of unilateral transradial amputees actively controlling a prosthesis in real-time to complete a functional task. Overall, we found that mirror training resulted in significantly faster task completion speeds and similar subjective workload. These results demonstrate that mirror training can potentially provide more dexterous control through the utilization of task-specific, user-selected training data. Consequently, these findings serve as a valuable guide for the next generation of myoelectric and neuroprostheses leveraging machine learning to provide more dexterous and intuitive control.

Effects of Training and Calibration Data on Surface Electromyogram-Based Recognition for Upper Limb Amputees

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

Recent trends and challenges of surface electromyography in prosthetic applications

Surface electromyography (sEMG) meets extensive applications in the field of prosthesis in the current period. The effectiveness of sEMG in prosthesis applications has been verified by numerous revolutionary developments and extensive research attempts. A large volume of research and literature works have explored and validated the vast use of these signals in prostheses as an assistive technology. The objective of this paper is to conduct a systematic review and offer a detailed overview of the work record in the prosthesis and myoelectric interfaces framework. This review utilized a systematic search strategy to identify published articles discussing the state-of-the-art applications of sEMG in prostheses (including upper limb prosthesis and lower limb prostheses). Relevant studies were identified using electronic databases such as PubMed, IEEE Explore, SCOPUS, ScienceDirect, Google Scholar and Web of Science. Out of 3791 studies retrieved from the databases, 188 articles were found to be potentially relevant (after screening of abstracts and application of inclusion-exclusion criteria) and included in this review. This review presents an investigative analysis of sEMG-based prosthetic applications to assist the readers in making further advancements in this field. It also discusses the fundamental advantages and disadvantages of using sEMG in prosthetic applications. It also includes some important guidelines to follow in order to improve the performance of sEMG-based prosthesis. The findings of this study support the widespread use of sEMG in prosthetics. It is concluded that sEMG-based prosthesis technology, still in its sprouting phase, requires significant explorations for further development. Supplementary investigations are necessary in the direction of making a seamless mechanism of biomechatronics for sEMG-based prosthesis by cohesive efforts of robotic researchers and biomedical engineers.

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation

BACKGROUND

Despite the growing availability of multifunctional prosthetic hands, users' control and overall functional abilities with these hands remain limited. The combination of pattern recognition control and targeted muscle reinnervation (TMR) surgery, an innovative technique where amputated nerves are transferred to reinnervate new muscle targets in the residual limb, has been used to improve prosthesis control of individuals with more proximal upper limb amputations (i.e., shoulder disarticulation and transhumeral amputation).

OBJECTIVE

The goal of this study was to determine if prosthesis hand grasp control improves following transradial TMR surgery.

METHODS

Eight participants were trained to use a multi-articulating hand prosthesis under myoelectric pattern recognition control. All participated in home usage trials pre- and post-TMR surgery. Upper limb outcome measures were collected following each home trial.

RESULTS

Three outcome measures (Southampton Hand Assessment Procedure, Jebsen-Taylor Hand Function Test, and Box and Blocks Test) improved 9-12 months post-TMR surgery compared with pre-surgery measures. The Assessment of Capacity for Myoelectric Control and Activities Measure for Upper Limb Amputees outcome measures had no difference pre- and post-surgery. An offline electromyography analysis showed a decrease in grip classification error post-TMR surgery compared to pre-TMR surgery. Additionally, a majority of subjects noted qualitative improvements in their residual limb and phantom limb sensations post-TMR.

CONCLUSIONS

The potential for TMR surgery to result in more repeatable muscle contractions, possibly due to the reduction in pain levels and/or changes to phantom limb sensations, may increase functional use of many of the clinically available dexterous prosthetic hands.

Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals

Bio-signal based hand motion recognition plays a critical role in the tasks of human-machine interaction, such as the natural control of multifunctional prostheses. Although a large number of classification technologies have been taken to improve the motion recognition accuracy, it is still a challenge to achieve acceptable performance for multiple modality input. This study proposes a multi-modality deep forest (MMDF) framework to identify hand motions, in which surface electromyographic signals (sEMG) and acceleration signals (ACC) are fused at the input level. The proposed MMDF framework constitutes of three main stages, sEMG and ACC feature extraction, feature dimension reduction, and a cascade structure deep forest for classification. A public database "Ninapro DB7" is used to evaluate the performance of the proposed framework, and the experimental results show that it can achieve a significantly higher accuracy than that of competitors. Besides, our experimental results also show that MMDF outperforms other traditional classifiers with the input of the single modality of sEMG signals. In sum, this study verifies that ACC signals can be an excellent supplementary for sEMG, and MMDF is a plausible solution to fuse mulit-modality bio-signals for human motion recognition.

Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG

We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).

Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification

The myoelectric prosthesis is a promising tool to restore the hand abilities of amputees, but the classification accuracy of surface electromyography (sEMG) is not high enough for real-time application. Researchers proposed integrating sEMG signals with another feature that is not affected by amputation. The strong coordination between vision and hand manipulation makes us consider including visual information in prosthetic hand control. In this study, we identified a sweet period during the early reaching phase in which the vision data could yield a higher accuracy in classifying the grasp patterns. Moreover, the visual classification results from the sweet period could be naturally integrated with sEMG data collected during the grasp phase. After the integration, the accuracy of grasp classification increased from 85.5% (only sEMG) to 90.06% (integrated). Knowledge gained from this study encourages us to further explore the methods for incorporating computer vision into myoelectric data to enhance the movement control of prosthetic hands.

Embodiment of a virtual prosthesis through training using an EMG-based human-machine interface: Case series

Therapeutic strategies capable of inducing and enhancing prosthesis embodiment are a key point for better adaptation to and acceptance of prosthetic limbs. In this study, we developed a training protocol using an EMG-based human-machine interface (HMI) that was applied in the preprosthetic rehabilitation phase of people with amputation. This is a case series with the objective of evaluating the induction and enhancement of the embodiment of a virtual prosthesis. Six men and a woman with unilateral transfemoral traumatic amputation without previous use of prostheses participated in the study. Participants performed a training protocol with the EMG-based HMI, composed of six sessions held twice a week, each lasting 30 mins. This system consisted of myoelectric control of the movements of a virtual prosthesis immersed in a 3D virtual environment. Additionally, vibrotactile stimuli were provided on the participant’s back corresponding to the movements performed. Embodiment was investigated from the following set of measurements: skin conductance response (affective measurement), crossmodal congruency effect (spatial perception measurement), ability to control the virtual prosthesis (motor measurement), and reports before and after the training. The increase in the skin conductance response in conditions where the virtual prosthesis was threatened, recalibration of the peripersonal space perception identified by the crossmodal congruency effect, ability to control the virtual prosthesis, and participant reports consistently showed the induction and enhancement of virtual prosthesis embodiment. Therefore, this protocol using EMG-based HMI was shown to be a viable option to achieve and enhance the embodiment of a virtual prosthetic limb.

sEMG-based Gesture Recognition using Deep Learning from Noisy Labels

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset's quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMGMap input. (ii) deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

Age-dependent Upper Limb Myoelectric Control Capability in Typically Developing Children

Research in EMG-based control of prostheses has mainly utilized adult subjects who have fully developed neuromuscular control. Little is known about children's ability to generate consistent EMG signals necessary to control artificial limbs with multiple degrees of freedom. As a first step to address this gap, experiments were designed to validate and benchmark two experimental protocols that quantify the ability to coordinate forearm muscle contractions in typically developing children. Able-bodied, healthy adults and children participated in our experiments that aimed to measure an individual's ability to use myoelectric control interfaces. In the first experiment, participants performed 8 repetitions of 16 different hand/wrist movements. Using offline classification analysis based on Support Vector Machine, we quantified their ability to consistently produce distinguishable muscle contraction patterns. We demonstrated that children had a smaller number of highly independent movements (can be classified with >90% accuracy) than adults did. The second experiment measured participants' ability to control the position of a cursor on a 1-DoF virtual slide using proportional EMG control with three different visuomotor gain levels. We found that children had higher failure rates and slower average target acquisitions than adults did, primarily due to longer correction times that did not improve over repetitive practice. We also found that the performance in both experiments was age-dependent in children. The results of this study provide novel insights into the technical and empirical basis to better understand neuromuscular development in children with upper-limb loss.

Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG

Pattern recognition using surface Electromyography (sEMG) applied on prosthesis control has attracted much attention in these years. In most of the existing methods, the sEMG signal during the firmly grasped period is used for grasp classification because good performance can be achieved due to its relatively stable signal. However, using the only the firmly grasped period may cause a delay to control the prosthetic hand gestures. Regarding this issue, we explored how grasp classification accuracy changes during the reaching and grasping process, and identified the period that can leverage the grasp classification accuracy and the earlier grasp detection. We found that the grasp classification accuracy increased along the hand gradually grasping the object till firmly grasped, and there is a sweet period before firmly grasped period, which could be suitable for early grasp classification with reduced delay. On top of this, we also explored corresponding training strategies for better grasp classification in real-time applications.

Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

A bioelectric neural interface towards intuitive prosthetic control for amputees

OBJECTIVE

While prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.

APPROACH

Here we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject's motor intention.

MAIN RESULTS

A pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient's true intention.

SIGNIFICANCE

Our study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways. (Clinical trial identifier: NCT02994160).

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person's life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.

Pattern recognition and direct control home use of a multi-articulating hand prosthesis

Although more multi-articulating hand prostheses have become commercially available, replacing a missing hand remains challenging from a control perspective. This study investigated myoelectric direct control and pattern recognition home use of a multi-articulating hand prosthesis for individuals with a transradial amputation. Four participants were fitted with an i-limb Ultra Revolution hand and a Coapt COMPLETE CONTROL system. An occupational therapist provided training for each control style and how to use the various grips. The number of grips available to each individual was determined by clinician and user feedback to optimize both the number of grips available and the reliability of grip selection. Home trial data corresponding to individual usage were recorded. No significant differences were found between direct and pattern recognition control home trials in regards to trial length (p=0.96), days powered on (p=0.21), or total time powered on (p=0.91). There was a higher average number of configured grips for direct control at 4.8 [0.5] compared to 3.8 [0.5] for pattern recognition control, but this difference did not reach significance (p=0.092). Across all hand close movements, users spent a majority of time $(\gt80$%) in one grip when using direct control. For pattern recognition usage was spread across more grips $(\gt45$% time in one grip, 25% time in a 2^nd grip, and 20% time in a 3^rd grip). Pattern recognition control may provide users with a more intuitive way to select and use the various grips available to them.

Wire Electrodes Embedded in Artificial Conduit for Long-term Monitoring of the Peripheral Nerve Signal

Massive efforts to develop neural interfaces have been made for controlling prosthetic limbs according to the will of the patient, with the ultimate goal being long-term implantation. One of the major struggles is that the electrode’s performance degrades over time due to scar formation. Herein, we have developed peripheral nerve electrodes with a cone-shaped flexible artificial conduit capable of protecting wire electrodes from scar formation. The wire electrodes, which are composed of biocompatible alloy materials, were embedded in the conduit where the inside was filled with collagen to allow the damaged nerves to regenerate into the conduit and interface with the wire electrodes. After implanting the wire electrodes into the sciatic nerve of a rat, we successfully recorded the peripheral neural signals while providing mechanical stimulation. Remarkably, we observed the external stimuli-induced nerve signals at 19 weeks after implantation. This is possibly due to axon regeneration inside our platform. To verify the tissue response of our electrodes to the sciatic nerve, we performed immunohistochemistry (IHC) and observed axon regeneration without scar tissue forming inside the conduit. Thus, our strategy has proven that our neural interface can play a significant role in the long-term monitoring of the peripheral nerve signal.

Virtual Integration Environment as an Advanced Prosthetic Limb Training Platform

Background: Despite advances in prosthetic development and neurorehabilitation, individuals with upper extremity (UE) loss continue to face functional and psychosocial challenges following amputation. Recent advanced myoelectric prostheses offer intuitive control over multiple, simultaneous degrees of motion and promise sensory feedback integration, but require complex training to effectively manipulate. We explored whether a virtual reality simulator could be used to teach dexterous prosthetic control paradigms to individuals with UE loss. Methods: Thirteen active-duty military personnel with UE loss (14 limbs) completed twenty, 30-min passive motor training sessions over 1-2 months. Participants were asked to follow the motions of a virtual avatar using residual and phantom limbs, and electrical activity from the residual limb was recorded using surface electromyography. Eight participants (nine limbs), also completed twenty, 30-min active motor training sessions. Participants controlled a virtual avatar through three motion sets of increasing complexity (Basic, Advanced, and Digit) and were scored on how accurately they performed requested motions. Score trajectory was assessed as a function of time using longitudinal mixed effects linear regression. Results: Mean classification accuracy for passive motor training was 43.8 ± 10.7% (14 limbs, 277 passive sessions). In active motor sessions, >95% classification accuracy (which we used as the threshold for prosthetic acceptance) was achieved by all participants for Basic sets and by 50% of participants in Advanced and Digit sets. Significant improvement in active motor scores over time was observed in Basic and Advanced sets (per additional session: β-coefficient 0.125, p = 0.022; β-coefficient 0.45, p = 0.001, respectively), and trended toward significance for Digit sets (β-coefficient 0.594, p = 0.077). Conclusions: These results offer robust evidence that a virtual reality training platform can be used to quickly and efficiently train individuals with UE loss to operate advanced prosthetic control paradigms. Participants can be trained to generate muscle contraction patterns in residual limbs that are interpreted with high accuracy by computer software as distinct active motion commands. These results support the potential viability of advanced myoelectric prostheses relying on pattern recognition feedback or similar controls systems.

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.

Effect of clinical parameters on the control of myoelectric robotic prosthetic hands

Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data

Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analyzed the repeatability of sEMG classification of hand grasps. The main goals of this paper are to explore repeatability in sEMG data and to release a repeatability database with the recorded experiments. The data are recorded from 10 intact subjects repeating 7 grasps 12 times, twice a day for 5 days. The data are publicly available on the Ninapro web page. The analysis for the repeatability is based on the comparison of movement classification accuracy in several data acquisitions and for different subjects. The analysis is performed using mean absolute value and waveform length features and a Random Forest classifier. The accuracy obtained by training and testing on acquisitions at different times is on average 27.03% lower than training and testing on the same acquisition. The results obtained by training and testing on different acquisitions suggest that previous acquisitions can be used to train the classification algorithms. The inter-subject variability is remarkable, suggesting that specific characteristics of the subjects can affect repeatibility and sEMG classification accuracy. In conclusion, the results of this paper can contribute to develop more robust control systems for hand prostheses, while the presented data allows researchers to test repeatability in further analyses.

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral (R²=0.72) and mirrored bilateral (R²=0.72) reaches and of forearm pronation/supination during unilateral (R²=0.77) and mirrored bilateral (R²=0.70) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral (R²=0.82) and mirrored bilateral (R²=0.75) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension (R²=0.72) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination (R²=0.88) for these subjects, a contradictory result that merits further study.

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

In the frame of assistive robotics, multi-finger prosthetic hand/wrists have recently appeared, offering an increasing level of dexterity; however, in practice their control is limited to a few hand grips and still unreliable, with the effect that pattern recognition has not yet appeared in the clinical environment. According to the scientific community, one of the keys to improve the situation is multi-modal sensing, i.e., using diverse sensor modalities to interpret the subject's intent and improve the reliability and safety of the control system in daily life activities. In this work, we first describe and test a novel wireless, wearable force- and electromyography device; through an experiment conducted on ten intact subjects, we then compare the obtained signals both qualitatively and quantitatively, highlighting their advantages and disadvantages. Our results indicate that force-myography yields signals which are more stable across time during whenever a pattern is held, than those obtained by electromyography. We speculate that fusion of the two modalities might be advantageous to improve the reliability of myocontrol in the near future.

The Ninapro database: A resource for sEMG naturally controlled robotic hand prosthetics

The dexterous natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: surface electromyography gives some control capabilities but these are limited, often not natural and require long training times; the application of pattern recognition techniques recently started to be applied in practice. While results in the scientific literature are promising they have to be improved to reach the real needs. The Ninapro database aims to improve the field of naturally controlled robotic hand prosthetics by permitting to worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark database. Currently, the Ninapro database includes data from 67 intact subjects and 11 amputated subject performing approximately 50 different movements. The data are aimed at permitting the study of the relationships between surface electromyography, kinematics and dynamics. The Ninapro acquisition protocol was created in order to be easy to be reproduced. Currently, the number of datasets included in the database is increasing thanks to the collaboration of several research groups.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player's performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

Classification of hand movements in amputated subjects by sEMG and accelerometers

Numerous recent studies have aimed to improve myoelectric control of prostheses. However, the majority of these studies is characterized by two problems that could be easily fulfilled with recent resources supplied by the scientific literature. First, the majority of these studies use only intact subjects, with the unproved assumption that the results apply equally to amputees. Second, usually only electromyography data are used, despite other sensors (e.g., accelerometers) being easy to include into a real life prosthesis control system. In this paper we analyze the mentioned problems by the classification of 40 hand movements in 5 amputated and 40 intact subjects, using both sEMG and accelerometry data and applying several different state of the art methods. The datasets come from the NinaPro database, which supplies publicly available sEMG data to develop and test machine learning algorithms for prosthetics. The number of subjects can seem small at first sight, but it is not considering the literature of the field (which has to face the difficulty of recruiting trans-radial hand amputated subjects). Our results indicate that the maximum average classification accuracy for amputated subjects is 61.14%, which is just 15.86% less than intact subjects, and they show that intact subjects results can be used as proxy measure for amputated subjects. Finally, our comparison shows that accelerometry as a modality is less affected by amputation than electromyography, suggesting that real life prosthetics performance may easily be improved by inclusion of accelerometers.

Electromyography data for non-invasive naturally-controlled robotic hand prostheses

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.

Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom

OBJECTIVE

Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs).

APPROACH

Ten able-bodied subjects and one person with congenital upper-limb deficiency took part in this study, and performed wrist movements with various combinations of two DoFs (flexion/extension and radial/ulnar deviation). During the experiment, high density electromyographic (EMG) signals and the actual wrist angles were recorded with an 8 × 24 electrode array and a motion tracking system, respectively. The wrist angles were estimated from EMG features with ridge regression using the subsets of channels chosen by three different channel selection methods: (1) least absolute shrinkage and selection operator (LASSO), (2) sequential feature selection (SFS), and (3) uniform selection (UNI).

MAIN RESULTS

SFS generally showed higher estimation accuracy than LASSO and UNI, but LASSO always outperformed SFS in terms of robustness, such as noise addition, channel shift and training data reduction. It was also confirmed that about 95% of the original performance obtained using all channels can be retained with only 12 bipolar channels individually selected by LASSO and SFS.

SIGNIFICANCE

From the analysis results, it can be concluded that LASSO is a promising channel selection method for accurate simultaneous and proportional prosthesis control. We expect that our results will provide a useful guideline to select optimal channel subsets when developing clinical myoelectric prosthesis control systems based on continuous movements with multiple DoFs.

Neural correlates of the rubber hand illusion in amputees: a report of two cases

One of the current challenges in the field of advanced prosthetics is the development of artificial limbs that provide the user with detailed sensory feedback. Sensory feedback from our limbs is not only important for proprioceptive awareness and motor control, but also essential for providing us with a feeling of ownership or simply put, the sensation that our limbs actually belong to ourselves. The strong link between sensory feedback and ownership has been repeatedly demonstrated with the so-called rubber hand illusion (RHI), during which individuals are induced with the illusory sensation that an artificial hand is their own. In healthy participants, this occurs via integration of visual and tactile signals, which is primarily supported by multisensory regions in premotor and intraparietal cortices. Here, we describe a functional magnetic resonance imaging (fMRI) study with two upper limb amputees, showing for the first time that the same brain regions underlie ownership sensations of an artificial hand in this population. Albeit preliminary, these findings are interesting from both a theoretical as well as a clinical point of view. From a theoretical perspective, they imply that even years after the amputation, a few seconds of synchronous visuotactile stimulation are sufficient to activate hand-centered multisensory integration mechanisms. From a clinical perspective, they show that a very basic sensation of touch from an artificial hand can be obtained by simple but precisely targeted stimulation of the stump, and suggest that a similar mechanism implemented in prosthetic hands would greatly facilitate ownership sensations and in turn, acceptance of the prosthesis.

A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art

BACKGROUND

Current clinical myoelectric systems provide unnatural prosthesis control, with limited functionality. In this study, we propose a proportional state-based control method, which allows switching between functions in a more natural and intuitive way than the traditional co-contraction switch method.

METHODS

We validated the ability of the proposed system to provide precise control in both position and velocity modes. Two tests were performed with online visual feedback, involving target reaching and direct force control in grasping. The performance of the system was evaluated both on a subject with limb deficiency and in 9 intact-limbed subjects, controlling two degrees of freedom (DoF) of the hand and wrist.

RESULTS

The system allowed completion of the tasks involving 1-DoF with task completion rate >96% and of those involving 2-DoF with completion rate >91%. When compared with the clinical/industrial state-of-the-art approach and with a classic pattern recognition approach, the proposed method significantly improved the performance in the 2-DoF tasks. The completion rate in grasping force control was >97% on average.

CONCLUSIONS

These results indicate that, using the proposed system, subjects were successfully able to operate two DoFs, and to achieve precise force control in grasping. Thus, the proposed state-based method could be a suitable alternative for commercial myoelectric devices, providing reliable and intuitive control of two DoFs.

The multiple silicone tube device, "tubes within a tube," for multiplication in nerve reconstruction

Multiple nerve branches were created during the regeneration procedure after a nerve injury and such multiple branches are suggested to be used to control, for example, prosthesis with many degrees of freedom. Transected rat sciatic nerve stumps were inserted into a nine mm long silicone tube, which contained four, five mm long, smaller tubes, thus leaving a five mm gap for regenerating nerve fibers. Six weeks later, several new nerve structures were formed not only in the four smaller tubes, but also in the spaces in-between. The 7–9 new continuous nerve structures, which were isolated as individual free nerves after removal of the tubes, were delineated by a perineurium and contained both myelinated and unmyelinated nerve fibers as well as blood vessels. Stimulation of the proximal nerve elicited contractions in distal muscles. Thin metal electrodes, inserted initially into the smaller tubes in some experiments, became embedded in the new nerve structures and when stimulated contractions of the distal muscles were observed. The “tubes within a tube” technique, creating multiple new nerves from a single “mother” nerve, can be used to record multiple signals for prosthetic device control or as sources for supply of multiple denervated targets.

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Reduction of the effect of arm position variation on real-time performance of motion classification

A couple of studies have been conducted with able-bodied subjects and/or arm amputees to investigate the impact of arm position changes in the practical use of a multifunctional myoelectric prosthesis. The classification accuracy calculated offline from electromyography (EMG) recordings was used as a performance metric in these studies, which is not a true measure of real-time control performance. In this study, the influence of arm position changes on the real-time performance of EMG pattern recognition (EMG-PR) control was quantitatively evaluated with four real-time metrics including motion response time, motion completion time, motion completion rate, and dynamic efficiency. Ten able-bodied subjects participated in the study and a cascade classifier built with both EMG and mechanomyogram (MMG) recordings was proposed to reduce the impact of arm position variation. The pilot results showed that arm position changes would substantially affect the real-time performance of EMG pattern-recognition based prosthesis control. Using a cascade classifier could significantly increase the average real-time completion rate (p-value&#60;0.01). This suggests that the proposed cascade classifier may have potential to reduce the influence of arm position variation on the real-time control performance of a prosthesis.

Preliminary functional assessment of a multigrasp myoelectric prosthesis

The authors have previously described a multigrasp hand prosthesis prototype, and a two-site surface EMG based multigrasp control interface for its control. In this paper, the authors present a preliminary assessment of the efficacy of the prosthesis and multigrasp controller in performing tasks requiring interaction and manipulation. The authors use as a performance measure the Southampton Hand Assessment Procedure (SHAP), which entails manipulation of various objects designed to emulate activities of daily living, and provides a set of scores that indicate level of functionality in various types of hand function. In this preliminary assessment, a single non-amputee subject performed the SHAP while wearing the multigrasp prosthesis via an able-bodied adaptor. The results from this testing are presented, and compared to recently published SHAP results obtained with commercially available single-grasp and multigrasp prosthetic hands.

Evidence of muscle synergies during human grasping

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

Background

Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts.

Methods

With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated.

Results

With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms.

Conclusions

The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance.

Using ultrasound images of the forearm to predict finger positions

Medical ultrasound imaging is a well-known technique to gather live views of the interior of the human body. It is totally safe, it provides high spatial and temporal resolution, and it is nowadays available at any hospital. This suggests that it could be used as a human-computer interface. In this paper, we use ultrasound images of the human forearm to predict the finger positions, including thumb adduction and thumb rotation. Our experimental results show that there is a clear linear relationship between the features we extract from the images, and finger positions, expressed as angles at the metacarpo-phalangeal joints. The method is uniformly valid for all subjects considered. The unavoidable movements of the ultrasound probe with respect to the skin and of the skin with respect to the inner musculoskeletal structure are compensated for using the optical flow. Typical applications of this system range from teleoperated fine manipulation to finger stiffness estimation to ergonomy. If successfully applied to transradial amputees, it could be also used to reconstruct the imaginary limb, paving the way to, e.g., fine control of hand prostheses, treatment of neuropathic/phantom limb pain and visualization of the imaginary limb as a tool for the neuroscientist.

EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees

We propose a method for estimating wrist kinematics during dynamic wrist contractions from multi-channel surface electromyography (EMG). The algorithm extracts features from the surface EMG and uses dedicated multi-layer perceptron networks to estimate individual joint angles of the 3 degrees of freedom (DoFs) of the wrist. The method was designed with the aim of proportional and simultaneous control of multiple DoFs of active prostheses by unilateral amputees. Therefore, the proposed approach was tested in both unilateral transradial amputees and in intact-limbed control subjects. It was shown that the joint angles at the 3 DoFs of amputees can be estimated from surface EMG recordings , during mirrored bi-lateral contractions that simultaneously and proportionally articulated the 3 DoFs. The estimation accuracies of amputee subjects with long stumps were 62.5% ± 8.50% across all 3 DoFs, while accuracies of the intact-limbed control subjects were 72.0% ± 8.29%. The estimation results from intact-limbed subjects were consistent with earlier studies. The results from the current study demonstrated the feasibility of the proposed myoelectric control approach to provide a more intuitive myoelectric control strategy for unilateral transradial amputees.

On the viability of implantable electrodes for the natural control of artificial limbs: review and discussion

The control of robotic prostheses based on pattern recognition algorithms is a widely studied subject that has shown promising results in acute experiments. The long-term implementation of this technology, however, has not yet been achieved due to practical issues that can be mainly attributed to the use of surface electrodes and their highly environmental dependency. This paper describes several implantable electrodes and discusses them as a solution for the natural control of artificial limbs. In this context "natural" is defined as producing control over limb movement analogous to that of an intact physiological system. This includes coordinated and simultaneous movements of different degrees of freedom. It also implies that the input signals must come from nerves or muscles that were originally meant to produce the intended movement and that feedback is perceived as originating in the missing limb without requiring burdensome levels of concentration. After scrutinizing different electrode designs and their clinical implementation, we concluded that the epimysial and cuff electrodes are currently promising candidates to achieving a long-term stable and natural control of robotic prosthetics, provided that communication from the electrodes to the outside of the body is guaranteed.

Multigrasp myoelectric control for a transradial prosthesis

This paper presents the design and preliminary experimental verification of a multigrasp myoelectric controller. The controller enables the direct and proportional control of a multigrasp transradial prosthesis through a set of nine postures using two surface EMG electrodes. Five healthy subjects utilized the multigrasp controller to manipulate a virtual prosthesis to experimentally quantify the performance of the controller in terms of real time performance metrics. For comparison, the performance of the native hand was also characterized using a dataglove. The average overall transition times for the multigrasp myoelectric controller and the native hand with the dataglove were found to be 1.49 and 0.81 seconds, respectively. The transition rates for both were found to be the same (99.2%).

High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control

Pattern recognition based control of powered upper limb myoelectric prostheses offers a means of extracting more information from the available muscles than conventional methods. By identifying repeatable patterns of muscle activity across multiple muscle sites rather than relying on independent EMG signals it is possible to provide more natural, reliable control of myoelectric prostheses. The purposes of this study were to (1) determine if participants can perform distinctive muscle activation patterns associated with multiple wrist and hand movements reliably and (2) to show that high density EMG can be applied individually to determine the electrode location of a clinically acceptable number of electrodes (maximally eight) to classify multiple wrist and hand movements reliably in transradial amputees. Eight normally limbed subjects (five female, three male) and four transradial amputee subjects (two traumatic and congenital) subjects participated in this study, which examined the classification accuracies of a pattern recognition control system. It was found that tasks could be classified with high accuracy (85-98%) with normally limbed subjects (10-13 tasks) and with amputees (4-6) tasks. In healthy subjects, reducing the number of electrodes to eight did not affect accuracy significantly when those electrodes were optimally placed, but did reduce accuracy significantly when those electrodes were distributed evenly. In the amputee subjects, reducing the number of electrodes up to 4 did not affect classification accuracy or the number of tasks with high accuracy, independent of whether those remaining electrodes were evenly distributed or optimally placed. The findings in healthy subjects suggest that high density EMG testing is a useful tool to identify optimal electrode sites for pattern recognition control, but its use in amputees still has to be proven. Instead of just identifying the electrode sites where EMG activity is strong, clinicians will be able to choose the electrode sites that provide the most important information for classification.

A method for the control of multigrasp myoelectric prosthetic hands

This paper presents the design and preliminary experimental validation of a multigrasp myoelectric controller. The described method enables direct and proportional control of multigrasp prosthetic hand motion among nine characteristic postures using two surface electromyography electrodes. To assess the efficacy of the control method, five nonamputee subjects utilized the multigrasp myoelectric controller to command the motion of a virtual prosthesis between random sequences of target hand postures in a series of experimental trials. For comparison, the same subjects also utilized a data glove, worn on their native hand, to command the motion of the virtual prosthesis for similar sequences of target postures during each trial. The time required to transition from posture to posture and the percentage of correctly completed transitions were evaluated to characterize the ability to control the virtual prosthesis using each method. The average overall transition times across all subjects were found to be 1.49 and 0.81 s for the multigrasp myoelectric controller and the native hand, respectively. The average transition completion rates for both were found to be the same (99.2%). Supplemental videos demonstrate the virtual prosthesis experiments, as well as a preliminary hardware implementation.

Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: a preliminary study

BACKGROUND

Electromyography (EMG) pattern recognition offers the potential for improved control of multifunction myoelectric prostheses. However, it is unclear whether this technology can be successfully used by congenital amputees.

OBJECTIVE

The purpose of this investigation was to assess the ability of congenital transradial amputees to control a virtual multifunction prosthesis using EMG pattern recognition and compare their performance to that of acquired amputees from a previous study.

STUDY DESIGN

Preliminary cross-sectional study.

METHODS

Four congenital transradial amputees trained and tested a linear discriminant analysis (LDA) classifier with four wrist movements, five hand movements, and a no-movement class. Subjects then tested the classifier in real time using a virtual arm.

RESULTS

Performance metrics for the residual limb were poorer than those with the intact limb (classification accuracy: 52.1% ± 15.0% vs. 93.2% ± 15.8%; motion-completion rate: 49.0%± 23.0% vs. 84.0% ± 9.4%; motion-completion time: 2.05 ± 0.75 s vs. 1.13 ± 0.05 s, respectively). On average, performance with the residual limb by congenital amputees was reduced compared to that reported for acquired transradial amputees. However, one subject performed similarly to acquired amputees.

CONCLUSIONS

Pattern recognition control may be a viable option for some congenital amputees. Further study is warranted to determine success factors.

Neural interfaces for control of upper limb prostheses: the state of the art and future possibilities

Current treatment of upper limb amputation restores some degree of functional ability, but this ability falls far below the standard set by the natural arm. Although acceptance rates can be high when patients are highly motivated and receive proper training and care, current prostheses often fail to meet the daily needs of amputees and frequently are abandoned. Recent advancements in science and technology have led to promising methods of accessing neural information for communication or control. Researchers have explored invasive and noninvasive methods of connecting with muscles, nerves, or the brain to provide increased functionality for patients experiencing disease or injury, including amputation. These techniques offer hope of more natural and intuitive prosthesis control, and therefore increased quality of life for amputees. In this review, we discuss the current state of the art of neural interfaces, particularly those that may find application within the prosthetics field.

A biomechanical model for the development of myoelectric hand prosthesis control systems

Advanced myoelectric hand prostheses aim to reproduce as much of the human hand's functionality as possible. Development of the control system of such a prosthesis is strongly connected to its mechanical design; the control system requires accurate information on the prosthesis' structure and the surrounding environment, which can make development difficult without a finalized mechanical prototype. This paper presents a new framework for the development of electromyographic hand control systems, consisting of a prosthesis model based on the biomechanical structure of the human hand. The model's dynamic structure uses an ellipsoidal representation of the phalanges. Other features include underactuation in the fingers and thumb modeled with bond graphs, and a viscoelastic contact model. The model's functions are demonstrated by the execution of lateral and tripod grasps, and evaluated with regard to joint dynamics and applied forces. Finally, future work is suggested with which this model can be used in mechanical design and patient training as well.