Myoelectric Control Strategies Research Articles

Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

Background and ObjectiveMobility of subject (MoS) and muscle contraction force variation (MCFV) have been shown to individually degrade the performance of multiple degrees of freedom electromyogram (EMG) pattern recognition (PR) based prostheses control systems. Though these factors (MoS-MCFV) co-exist simultaneously in the practical use of the prosthesis, their combined impact on PR-based system has rarely been studied especially in the context of amputees who are the target users of the device. MethodsTo address this problem, this study systematically investigated the co-existing impact of MoS-MCFV on the performance of PR-based movement intent classifier, using EMG recordings acquired from eight participants who performed multiple classes of targeted limb movements across static and non-static scenarios with three distinct muscle contraction force levels. Then, a robust feature extraction method that is invariant to the combined effect of MoS-MCFV, namely, invariant time-domain descriptor (invTDD), was proposed to optimally characterize the multi-class EMG signal patterns in the presence of both factors. ResultsExperimental results consistently showed that the proposed invTDD method could significantly mitigate the co-existing impact of MoS-MCFV on PR-based movement-intent classifier with error reduction in the range of 7.50%~17.97% (p<0.05), compared to the commonly applied methods. Further evaluation using 2-dimentional principal component analysis (PCA) technique, revealed that the proposed invTDD method has obvious class-separability in the PCA feature space, with a significantly lower standard error (0.91%) compared to the existing methods. ConclusionThis study offers compelling insight on how to develop accurately robust multiple degrees of freedom control scheme for multifunctional prostheses that would be clinically viable. Also, the study may spur positive advancement in other application areas of medical robotics that adopts myoelectric control schemes such as the electric wheelchair and human-computer-interaction systems.

Proprioceptive Sonomyographic Control: A novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper\xa0extremity limb loss

Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographiccontrol. Unlike myoelectric control strategies which measure electrical activation of muscles and use the extracted signals to determine the velocity of an end-effector; our sonomyography-based strategy measures mechanical muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Therefore, our sonomyography-based control is congruent with a prosthetic user’s innate proprioception of muscle deformation in the residual limb. In this work, we evaluated proprioceptive sonomyographic control with 5 prosthetic users and 5 able-bodied participants in a virtual target achievement and holding task for 5 different hand motions. We observed that with limited training, the performance of prosthetic users was comparable to that of able-bodied participants and thus conclude that proprioceptive sonomyographic control is a robust and intuitive prosthetic control strategy.

Synergy-Based Myocontrol of a Multiple Degree-of-Freedom Humanoid Robot for Functional Tasks.

In the context of sensor-based human-robot interaction, a particularly promising solution is represented by myoelectric control schemes based on synergy-derived signals. We developed and tested on healthy subjects a synergy-based control to achieve simultaneous, continuous actuation of three degrees of freedom of a humanoid robot, while performing functional reach-to-grasp movements. The control scheme exploits subject-specific muscle synergies extracted from eleven upper limb muscles through an easy semi-supervised calibration phase, and computes online activation coefficients to actuate the robot joints. The humanoid robot was able to well reproduce the subjects' motion, which consisted in free multi-degree-of-freedom reach-to-grasp movements at self-paced speeds. Furthermore, the synergy-based online control significantly outperformed a traditional muscle-pair approach (that uses a pair of antagonist muscles for each joint), in terms of decreased error, increased correlation, and peak correlation between the subjects' and the robot's joint angles. The delay introduced by the two algorithms was comparable. This work is a proof-of-concept for an intuitive and robust myocontrol interface, without the need for any training and practice. It has several potential applications, especially for functional assistive engaging devices in children with social and motor impairments.

On the robustness of real-time myoelectric control investigations: a multiday Fitts’ law approach

Objective. Real-time myoelectric experimental protocol is considered as a means to quantify usability of myoelectric control schemes. While usability should be considered over time to assure clinical robustness, all real-time studies reported thus far are limited to a single session or day and thus the influence of time on real-time performance is still unexplored. In this study, the aim was to develop a novel experimental protocol to quantify the effect of time on real-time performance measures over multiple days using a Fitts’ law approach. Approach. Four metrics: throughput, completion rate, path efficiency and overshoot, were assessed using three train-test strategies: (i) an artificial neural network (ANN) classifier was trained on data collected from the previous day and tested on present day (BDT) (ii) trained and tested on the same day (WDT) and (iii) trained on all previous days including present day and tested on present day (CDT) in a week-long experimental protocol. Main results. It was found that on average, the completion rate (98.37% ± 1.47%) of CDT was significantly better (P < 0.01) than that of BDT (86.25% ± 3.46%) and WDT (94.22% ± 2.74%). The throughput (0.40 ± 0.03 bits s−1) of CDT was significantly better (P = 0.001) than that of BDT (0.38 ± 0.03 bits s−1). Offline analysis showed a different trend due to the difference in the training strategies. Significance. Results suggest that increasing the size of the training set over time can be beneficial to assure robust performance of the system over time.

A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands**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.

Objective. The conventional myoelectric control scheme of hand prostheses provides a high level of robustness during continuous use. Typically, the electrical activity of an agonist/antagonist muscle pair in the forearm is detected and used to control either opening/closing or rotation of the prosthetic hand. The translation of more sophisticated control approaches (e.g. regression-based classifiers) to clinical practice is limited mainly because of their lack of robustness in real-world conditions (e.g. due to different arm positions). We therefore explore a new hybrid approach, in which a second degree of freedom (DOF) controlled by the myoelectric activity of the posterior auricular muscles is added to the conventional forearm control. With this, an independent, simultaneous and proportional control of rotation and opening/closing of the hand is possible. Approach. In this study, we compared the hybrid auricular control system (hACS) to the two most commonly used control techniques for two DOF. Ten able-bodied subjects and one person with transradial amputation performed two standardizes tests in three different arm positions. Main results. Subjects controlled a hand prosthesis significantly more rapidly and more accurately using the hACS. Moreover, the robustness of the system was not influenced by different arm positions. Significance. The hACS therefore offers an alternative solution for simultaneous and proportional myoelectric control of two degrees of freedom that avoids several robustness issues related to machine learning based approaches.

Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG

Advances in myoelectric interfaces have increased the use of wearable prosthetics including robotic arms. Although promising results have been achieved with pattern recognition-based control schemes, control robustness requires improvement to increase user acceptance of prosthetic hands. The aim of this study was to quantify the performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique used to improve myoelectric control and to compare multiday surface electromyography (sEMG) and intramuscular (iEMG) recordings. Ten able-bodied and six amputee subjects with average ages of 24.5 and 34.5 years, respectively, were evaluated using offline classification error as the performance matric. Surface and intramuscular EMG were concurrently recorded while each subject performed 11 hand motions. Performance of SSAE was compared with that of linear discriminant analysis (LDA) classifier. Within-day analysis showed that SSAE (1.38 ± 1.38%) outperformed LDA (8.09 ± 4.53%) using both the sEMG and iEMG data from both able-bodied and amputee subjects (p &lt; 0.001). In the between-day analysis, SSAE outperformed LDA (7.19 ± 9.55% vs. 22.25 ± 11.09%) using both sEMG and iEMG data from both able-bodied and amputee subjects. No significant difference in performance was observed for within-day and pairs of days with eight-fold validation when using iEMG and sEMG with SSAE, whereas sEMG outperformed iEMG (p &lt; 0.001) in between-day analysis both with two-fold and seven-fold validation schemes. The results obtained in this study imply that SSAE can significantly improve the performance of pattern recognition-based myoelectric control scheme and has the strength to extract deep information hidden in the EMG data.

Characterizing Residual Muscle Properties in Lower Limb Amputees Using High Density EMG Decomposition: A Pilot Study.

As research is progressing towards EMG control of lower limb prostheses, it is vital to understand the neurophysiology of the residual muscles in the amputated limb, which has been largely ignored. Therefore, the goal of this study was to characterize the activation patterns (muscle recruitment and motor unit discharge patterns) of the residual muscles of lower limb amputees. One transtibial amputee subject was recruited for this pilot study. The participant wore three high-density EMG electrode pads (8x8 grid with 64 channels) on each limb (a total of six pads) - one on the tibialis anterior (TA), medial gastrocnemius (MG), and lateral gastrocnemius (LG), respectively. The participant was asked to follow a ramping procedure plateauing at 50% of maximum voluntary contraction (MVC) for both the TA and Gastrocnemius muscles. The EMG signals were then decomposed offline; the firing rate and spatial activation patterns of the muscle were analyzed. Results showed slower and more variable firing rate in motor units of residual muscles than those of intact side. In addition, the spatial pattern of muscle activation differed between residual and intact muscles. These results indicate that surface EMG signals recorded from residual muscles present modified signal features from intact shank muscles, which should be considered when implementing myoelectric control schemes.

Performance of Combined Surface and Intramuscular EMG for Classification of Hand Movements.

The surface EMG (sEMG) has been used as control source for upper limb prosthetics since decades. Previous studies suggested that intramuscular EMG showed promising results for upper limb prosthetics. This study investigates the strength of combined surface and intramuscular EMG (cEMG) for improved myoelectric control. Five able-bodied subjects and three transradial amputees were evaluated using offline classification error as performance metric. Six surface and intramuscular channels were recorded concurrently from each subject for seven consecutive days and Stacked sparse autoencoders (SSAE) and LDA classifiers were used for classification. As a control source, either sEMG channels were used or combined channels were used with reduced features using PCA. In the within session analysis, cEMG $( 2.21 \pm 1.19${%) outperformed the sEMG ($4.63 \pm 2.07${%) for both able-bodied and amputee subjects using SSAE. For between session analysis, cEMG outperformed the sEMG for both able-bodied and amputee subjects with percentage points difference of 7.93. These results imply cEMG can significantly improve the performance of pattern recognition based myoelectric control scheme for amputee subjects too and further improvement can be made by utilizing SSAE which show improved performance as compared to LDA.

A simple movement based control approach to ease the control of a myoelectric elbow prosthetics in transhumeral amputees

Common myoelectric control strategy remains complex to use and produce unnatural gestures since it does not allow simultaneous movements of several joints. This is especially an issue for the transhumeral amputees who can use an active elbow in addition to active prosthetic wrist and hand. Since most transhumeral amputees have a mobile residual limb, an interesting approach aims at utilizing this mobility to control the elbow. We thus developed a simple electronic device relying on an inertial measurement unit, that can be interfaced with existing commercial myoelectric elbows and which convert chosen displacements of the residual limb into control signals, offering an intuitive way of controlling an active elbow prosthesis whilst allowing a simultaneous myoelectric control of the wrist and hand. We performed an experiment with one transhumeral amputee wearing his myoelectric prosthetic (active elbow, wrist and hand) who was asked to grasp and transfer different objects several times while being recorded with a motion capture system. Theses task were performed twice: with a conventional full myoelectric control and with the proposed movement based control. Several metrics (position error, time, smoothness, along with body compensatory strategies) were used to quantify the performances obtained with these two modes. Analyses showed that the participant was able to perform the tasks in both condition, but that the movement based control approach allowed a reduction in task time and in exhibited body compensatory strategies, by offering an easier access to adjustments of the elbow posture compared to the full myoelectric control approach. This work illustrates the potential of using residual limb mobility in the control of prosthetics, to make it more natural and intuitive, and how this can have an indirect but positive impact on the whole body compensatory strategies exhibited by amputated subjects.

Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies.

On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency ( ), raw control with raw feedback resulted in stronger internal model development ( ), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.

Can We Achieve Intuitive Prosthetic Elbow Control Based on Healthy Upper Limb Motor Strategies?

Most transhumeral amputees report that their prosthetic device lacks functionality, citing the control strategy as a major limitation. Indeed, they are required to control several degrees of freedom with muscle groups primarily used for elbow actuation. As a result, most of them choose to have a one-degree-of-freedom myoelectric hand for grasping objects, a myoelectric wrist for pronation/supination, and a body-powered elbow. Unlike healthy upper limb movements, the prosthetic elbow joint angle, adjusted prior to the motion, is not involved in the overall upper limb movements, causing the rest of the body to compensate for the lack of mobility of the prosthesis. A promising solution to improve upper limb prosthesis control exploits the residual limb mobility: like in healthy movements, shoulder and prosthetic elbow motions are coupled using inter-joint coordination models. The present study aims to test this approach. A transhumeral amputated individual used a prosthesis with a residual limb motion-driven elbow to point at targets. The prosthetic elbow motion was derived from IMU-based shoulder measurements and a generic model of inter-joint coordinations built from healthy individuals data. For comparison, the participant also performed the task while the prosthetic elbow was implemented with his own myoelectric control strategy. The results show that although the transhumeral amputated participant achieved the pointing task with a better precision when the elbow was myoelectrically-controlled, he had to develop large compensatory trunk movements. Automatic elbow control reduced trunk displacements, and enabled a more natural body behavior with synchronous shoulder and elbow motions. However, due to socket impairments, the residual limb amplitudes were not as large as those of healthy shoulder movements. Therefore, this work also investigates if a control strategy whereby prosthetic joints are automatized according to healthy individuals' coordination models can lead to an intuitive and natural prosthetic control.

Differences in Myoelectric and Body-Powered Upper-Limb Prostheses: Systematic Literature Review

ABSTRACT The choice of a myoelectric or body-powered upper-limb prosthesis can be determined using factors including control, function, feedback, cosmesis, and rejection. Although body-powered and myoelectric control strategies offer unique functions, many prosthesis users must choose one. A systematic review was conducted to determine differences between myoelectric and body-powered prostheses to inform evidence-based clinical practice regarding prescription of these devices and training of users. A search of 9 databases identified 462 unique publications. Ultimately, 31 of them were included and 11 empirical evidence statements were developed. Conflicting evidence has been found in terms of the relative functional performance of body-powered and myoelectric prostheses. Body-powered prostheses have been shown to have advantages in durability, training time, frequency of adjustment, maintenance, and feedback; however, they could still benefit from improvements of control. Myoelectric prostheses have been shown to improve cosmesis and phantom-limb pain and are more accepted for light-intensity work. Currently, evidence is insufficient to conclude that either system provides a significant general advantage. Prosthetic selection should be based on a patient’s individual needs and include personal preferences, prosthetic experience, and functional needs. This work demonstrates that there is a lack of empirical evidence regarding functional differences in upper-limb prostheses.

Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses

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

Pre-clinical evaluation of a natural prosthetic elbow control strategy using residual limb motion and a model of healthy inter-joint coordinations

A gap has been growing between the mechanical features of newly commercialized prosthetic devices and the control strategies available to the users. The prosthetic joints are controlled sequentially via myoelectric control, and each actuation requires the user's attention. Because of a complex control scheme, transhumeral amputees are generally equipped with a 1-degree-of-freedom myoelectric hand, a myoelectric wrist rotator, and a manually locked elbow. The prosthetic forearm position, adjusted before the movement, is not involved in the overall upper limb movements, resulting in the development of compensatory strategies. A promising solution to improve prosthetic control utilizes the residual limb motions to control the elbow. Previous studies have shown that elbow motion could be predicted from measures of the residual limb movements and an inter-joint coordination model. This study is the first to report the utilization of an automatically driven prosthetic elbow by a transhumeral amputee. The participant pointed at targets with a prosthesis prototype including a modified motorized elbow. The prosthetic elbow motion was derived from a generic model of inter-joint coordinations, and IMU-based residual limb measurements. The participant performed also the task with the prosthetic elbow implemented with his own myoelectric control strategy. Body movements were assessed with the data recorded with a motion capture system. The patient achieved the pointing task with a better precision when the elbow was myoelectrically-driven. However, these movements required important trunk compensations. Trunk movements were smaller with residual limb motion-based elbow control, which enabled a more natural overall body behavior with synchronous shoulder and elbow motions. Due to socket impairment, but also to post-amputation body scheme modifications and discrepancies between healthy and artificial limbs, the participant's residual limb amplitudes were different of the ones of healthy shoulder movements for the same tasks. This work questions the paradigm whereby a prosthetic elbow can be intuitively and naturally used by an amputee while its motion is derived from healthy individual data. Although there is a need for novel modeling approaches to build an inter-joint coordination model adapted to each user, residual limb motion-driven prosthetic elbow enables simultaneous control of elbow and end-effector, and restores a more natural body behavior.

The effect of myoelectric prosthesis control strategies and feedback level on adaptation rate for a target acquisition task.

The long-term performance of myoelectric prostheses is related not only to the short-term performance of the controller, but also to the user's ability to learn and adapt to the system. Different control architectures may have inherent tradeoffs between their short-term performance and the amount of relevant feedback that informs this adaptation. In this study we focused on the ability of two common types of myoelectric control interfaces: raw control with raw feedback, such as a regression, and filtered control with filtered feedback, such as a classifier, to affect user adaptation. We evaluated trial-by-trial adaptation to self-generated errors during a multi degree-of-freedom target acquisition task by fitting a linear regression model to data collected from 24 able-bodied subjects. Subjects showed significantly higher adaptation behavior to self-generated errors when using raw control with a raw feedback strategy than when using filtered control with a filtered feedback strategy, which suggests that control strategies with more feedback allow for higher adaptation. These results support our hypothesis that feedback-rich control strategies allow users to better understand the myoelectric control system, which may enable better long-term performance.

RETRACTED ARTICLE: Muscular synergy classification and myoelectric control using high-order cross-cumulants

High-order statistics (HOS) are well suited for describing non-Gaussian random processes. These techniques are increasingly being employed in myoelectric research, on both time and frequency domain techniques. This work presents HOS-based techniques using only HOS time domain features to classify myoelectric signals. The auto-, cross- and full- (joint) third-order cumulants are evaluated as EMG-signal feature vectors to be compared between them. Four surface EMG signals were processed for classify motions from the upper limbs. Synergy among channels is characterized by the features in both auto and cross modes, and their incidences for classifying five or six movements are analyzed. In contrast to the third-order auto-cumulants, it had been verified that the third-order cross-cumulants have the same classification rate by working with five or six movements. A myoelectric control scheme and its experimental application were executed with normal and disabled subjects, reaching a classification rates of 90%, in average. Accuracy in online experiments was similar to the off-line classification rate.

Continuous Recognition of Multifunctional Finger and Wrist Movements in Amputee Subjects Based on sEMG and Accelerometry.

Background:While the classification of multifunctional finger and wrist movement based on surface electromyography (sEMG) signals in intact subjects can reach remarkable recognition rates, the performance obtained from amputated subjects remained low.Methods:In this paper, we proposed and evaluated the myoelectric control scheme of upper-limb prostheses by the continuous recognition of 17 multifunctional finger and wrist movements on 5 amputated subjects. Experimental validation was applied to select optimal features and classifiers for identifying sEMG and accelerometry (ACC) modalities under the windows-based analysis scheme. The majority vote is adopted to eliminate transient jumps and produces smooth output for window-based analysis scheme. Furthermore, principle component analysis was employed to reduce the dimension of features and to eliminate redundancy for ACC signal. Then a novel metric, namely movement error rate, was also employed to evaluate the performance of the continuous recognition framework proposed herein.Results:The average accuracy rates of classification were up to 88.7 ± 2.6% over 5 amputated subjects, which was an outstanding result in comparison with the previous literature.Conclusion:The proposed technique was proven to be a potential candidate for intelligent prosthetic systems, which would increase quality of life for amputated subjects.

Dynamic training protocol improves the robustness of PR-based myoelectric control

In pattern recognition (PR)-based myoelectric control schemes, the classifier is generally trained in ideal laboratory conditions, due to which the classification accuracy might be affected by confounding factors such as force variations, limb positions, and inadvertent electromyography (EMG) activation. Many endeavors have been put forward to mitigate this effect by adopting new training protocols that consider only quite a few independent factors. In this note, we propose a dynamic protocol, which embraces multiple EMG variations in data collection, to train a classifier with improved generalization ability. A total of four training protocols are examined, wherein affecting factors like upper-limb movements, contraction levels and inadvertent EMG activations are differently considered. Based on receiver operating characteristic (ROC) analysis, we came up with a new performance metric, ROC area rate (RAR), to directly inspect the accuracy and robustness of the classifiers obtained through different training protocols. Our results show that, compared with the other three protocols, the protocol with dynamic limb postures and dynamic muscle contractions (termed as DPDE) obtains the highest RAR (73.3%, on-way analysis of variance, p<0.005). Our results suggest that there is no need to integrate every EMG variation in the training protocol for receiving a robust EMG pattern recognition. Online control experiments with three amputees manipulating a multiple-DOF prosthetic hand also verify our findings.

Myoelectric intuitive control and transcutaneous electrical stimulation of the forearm for vibrotactile sensation feedback applied to a 3D printed prosthetic hand.

This paper presents the development of a myoelectric prosthetic hand based on a 3D printed model. A myoelectric control strategy based on artificial neural networks is implemented on a microcontroller for online position estimation. Position estimation performance achieves a correlation index of 0.78. Also a study involving transcutaneous electrical stimulation was performed to provide tactile feedback. A series of stimulations with controlled parameters were tested on five able-body subjects. A single channel stimulator was used, positioning the electrodes 8 cm on the wrist over the ulnar and median nerve. Controlling stimulation parameters such as intensity, frequency and pulse width, the subjects were capable of distinguishing different sensations over the palm of the hand. Three main sensations where achieved: tickling, pressure and pain. Tickling and pressure were discretized into low, moderate and high according to the magnitude of the feeling. The parameters at which each sensation was obtained are further discussed in this paper.

Classification of Multiple Finger Motions During Dynamic Upper Limb Movements.

To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.