Myoelectric Control Strategies Research Articles

Using principles of motor control to analyze performance of human machine interfaces

There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

Hierarchical strategy for sEMG classification of the hand/wrist gestures and forces of transradial amputees.

The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness. In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA). The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the "Hand/wrist gestures classifier" and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%. A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the "Hand/wrist gestures classifier", and the NLR with FE for the Spherical and Tip force classifiers.

Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control.

The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average [Formula: see text] of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.

Spectral Image-Based Multiday Surface Electromyography Classification of Hand Motions Using CNN for Human–Computer Interaction

Physiological signals such as electromyography (EMG) have been used in human–computer interaction (HCI) for medical applications. Wearable prostheses, such as robotic limbs, have seen a surge in popularity because of technological advancements in myoelectric interfaces. In spite of encouraging achievements with pattern-recognition-based control systems, user acceptability of prosthetic hands still needs improvement in control robustness. The purpose of this research is to compare multiday surface EMG (sEMG) recordings and measure the performance of convolutional neural network (CNN) to enhance myoelectric control. The performance metrics used in this study are accuracy, macro weighted precision (MWP), macro weighted recall (MWR), and macro <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}1$ </tex-math></inline-formula> -score for eight able-bodied (healthy and nondisabled) and four amputee subjects. Using Mel-spectrogram-based sEMG data from both the able-bodied (healthy and nondisabled) and amputee participants, our proposed CNN has achieved a mean classification accuracy of 99.42% ± 0.42% and 98.00% ± 0.58% for the able-bodied (healthy and nondisabled) and amputee subjects for the within-day analysis, respectively. The proposed CNN outperformed other classifiers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p} \leq0.05$ </tex-math></inline-formula> ) in the between-day analysis for twofold (65.88% ± 10.1% and 58.37% ± 9.11%) and for sevenfold validation (88.73% ± 1.43% and 77.35% ± 2.72%) using sEMG recordings from the able-bodied (healthy and nondisabled) and amputee subjects, respectively. The proposed CNN is compared with the pretrained transfer learning (TL) models and has achieved higher accuracy with lower computational cost. The results demonstrate that CNN can considerably increase the effectiveness of the pattern recognition myoelectric control schemes and can extract deep information from EMG data.

A Multi-User Transradial Functional-Test Socket for Validation of New Myoelectric Prosthetic Control Strategies.

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

Evoked Tactile Feedback and Control Scheme on Functional Utility of Prosthetic Hand

Fine manual control relies on intricate action-perception coupling to effectively interact with objects <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> Here, we evaluated how electrically evoked artificial tactile sensation can be integrated into the functional utility of a prosthetic hand. Using different myoelectric-control strategies, participants performed a modified box-and-block task using a prosthetic hand. Transcutaneous nerve stimulation was employed to elicit somatotopic fingertip tactile feedback reflecting prosthetic fingertip forces. This feedback was evoked using an electrode grid placed along the participants’ upper arm targeting the median and ulnar nerve bundles. Myoelectric signals from the finger flexor and extensor controlled the prosthetic joint velocity or position. Participants lifted, held, and transported cubes of varying weights using their minimum grip forces. The results showed that participants exerted lower forces and presented lower number of failed trials (prematurely dropped objects) when feedback was provided with respect to without feedback. We also found that position control required more flexor muscle activation compared with velocity control when tactile feedback was provided. Our findings reveal that non-invasively evoked tactile feedback could be used to effectively enable human-in-the-loop control of a prosthetic hand. The outcomes can provide a platform to characterize the action-perception couplings during prosthetic control, in order to improve user experience and system functionality.

A Novel Myoelectric Control Scheme Supporting Synchronous Gesture Recognition and Muscle Force Estimation.

Aiming to provide feasible solutions for the realization of the robust and natural myoelectric control systems, a novel myoelectric control scheme supporting gesture recognition and muscle force estimation is proposed in this study. Eleven grasping gestures abstracted from daily life are selected as the target gesture set. The high-density surface electromyography (HD-sEMG) of the forearm flexor and the grasping force signal are collected simultaneously. The synchronous prediction of gesture category and instantaneous force is realized by the multi-task learning (MTL) technique. Especially, a post-processing algorithm based on threshold method is conducted to overcome the influence of force variation on the accuracy of gesture recognition. The experimental results show that the proposed post-processing method can decrease the classification error significantly. Specifically, the overall gesture classification error is reduced by 27 ~ 30 percent compared with not using the post-processing method; and 16 ~ 24 percent compared with using classical post-processing methods. The whole scheme can realize the synchronous gesture recognition and force estimation with 9.35 ± 11.48% gesture classification error and 0.1479 ± 0.0436 root-mean-square deviation force estimation accuracy. Meanwhile, it is feasible in different number of electrodes and well meets the real-time requirement of the EMG control system in response time delay (about 28.22 ~ 113.16ms on average). The proposed framework provides the possibility for myoelectric control supporting synchronous gesture recognition and force estimation, which can be extended and applied in the fields of myoelectric prosthesis and exoskeleton devices.

The Influence of Training With Visual Biofeedback on the Predictability of Myoelectric Control Usability.

Studies have shown that closed-loop myoelectric control schemes can lead to changes in user performance and behavior compared to open-loop systems. When users are placed within the control loop, such as during real-time use, they must correct for errors made by the controller and learn what behavior is necessary to produce desired outcomes. Augmented feedback, consequently, has been used to incorporate the user throughout the training process and to facilitate learning. This work explores the effect of visual feedback presented during user training on both the performance and predictability of a myoelectric classification-based control system. Our results suggest that properly designed feedback mechanisms and training tasks can influence the quality of the training data and the ability to predict usability using linear combinations of metrics derived from feature space. Furthermore, our results confirm that the most common in-lab training protocol, screen guided training, may yield training data that are less representative of online use than training protocols that incorporate the user in the loop. These results suggest that training protocols should be designed that better parallel the testing environment to more effectively prepare both the algorithms and users for real-time control.

Object Recognition via Evoked Sensory Feedback during Control of a Prosthetic Hand.

Haptic and proprioceptive feedback is critical for sensorimotor integration when we use our hand to perform daily tasks. Here, we evaluated how externally evoked haptic and proprioceptive feedback and myoelectric control strategies affected the recognition of object properties when participants controlled a prosthetic hand. Fingertip haptic sensation was elicited using a transcutaneous nerve stimulation grid to encode the prosthetic's fingertip forces. An array of tactors elicited patterned vibratory stimuli to encode tactile-proprioceptive kinematic information of the prosthetic finger joint. Myoelectric signals of the finger flexor and extensor were used to control the position or velocity of joint angles of the prosthesis. Participants were asked to perform object property (stiffness and size) recognition, by controlling the prosthetic hand with concurrent haptic and tactile-proprioceptive feedback. With the evoked feedback, intact and amputee participants recognized the object stiffness and size at success rates ranging from 50% to 100% in both position and velocity control with no significant difference across control schemes. Our findings show that evoked somatosensory feedback in a non-invasive manner can facilitate closed-loop control of the prosthetic hand and allowed for simultaneous recognition of different object properties. The outcomes can facilitate our understanding on the role of sensory feedback during bidirectional human-machine interactions, which can potentially promote user experience in object interactions using prosthetic hands.

Comparison of Myoelectric Control Schemes for Simultaneous Hand and Wrist Movement using Chronically Implanted Electromyography: A Case Series.

A current biomedical engineering challenge is the development of a system that allows fluid control of multi-functional prosthetic devices through a human-machine interface. Here we probe this challenge by studying two subjects with trans-radial limb loss as they control a virtual hand and wrist system using 6 or 8 chronically implanted intramuscular electromyographic (iEMG) signals. The subjects successfully controlled a 4, 5, and 6 Degrees of Freedom (DoF's) virtual hand and wrist systems to perform a target matching task. Two control systems were evaluated where one tied EMG features directly to movement directions (Direct Control) and the other method determines user intent in the context of prior training data (Linear Interpolation). Subjects successfully matched most targets with both controllers but differences were seen as the complexity of the virtual limb system increased. The Direct Control method encountered difficulty due to crosstalk at higher DoF's. The Linear Interpolation method reduced crosstalk effects and outperformed Direct Control at higher DoF's. This work also studied the use of the Postural Control Algorithm to control the hand postures simultaneously with wrist degrees of freedom. This work presents preliminary evidence that the PC algorithm can be used in conjunction with wrist control, that Direct Control with iEMG signals allows stable 4-DoF control, and that EMG pre-processing using the Linear Interpolation method can improve performance at 5 and 6-DoF's.

Analyzing the impact of class transitions on the design of pattern recognition-based myoelectric control schemes

Despite continued efforts to improve classification accuracy, it has been reported that offline accuracy is a poor indicator of the usability of pattern recognition-based myoelectric control. One potential source of this disparity is the existence of transitions between contraction classes that happen during regular use and are reported to be problematic for pattern recognition systems. Nevertheless, these transitions are often ignored or undefined during both the training and testing processes. In this work, we propose a set of metrics for analyzing the transitions that occur during the voluntary changes between contraction classes during continuous control. These metrics quantify the common types of errors that occur during transitions and compare them to existing metrics that apply only to the steady-state portions of the data. We then use these metrics to analyze transition characteristics of 6 commonly used classifiers on a novel dataset that includes continuous transitions between all combinations of seven different contraction classes. Results show that a linear discriminant classifier consistently outperforms other conventional classifiers during both transitions and steady-state conditions, despite having an almost identical offline performance. Results also show that, although offline training metrics correlate with steady-state performance, they do not correlate with transition performance. These insights suggest that the proposed set of metrics could provide a shift in perspective on the way pattern recognition systems are evaluated and provide a more representative picture of a classifier’s performance, potentially narrowing the gap between offline performance and online usability.

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review.

The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics.

Hybrid Tongue - Myoelectric Control Improves Functional Use of a Robotic Hand Prosthesis.

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

Leveraging ANN and LDA Classifiers for Characterizing Different Hand Movements Using EMG Signals

The analysis of electromyographic (EMG) signals has expedited the use of a wearable prosthetic arm. To this end, pattern recognition-based myoelectric control schemes have shown the promising results; however, the choice of classifier and optimal features is always challenging. This paper presents the comparative analysis of classifiers for multiple EMG datasets including (1) the publicly accessible NinaPro database which provides data recorded for 52 hand movements collected from 27 subjects out of which twelve finger movements were classified, and (2) the data collected from ten healthy and six amputee subjects for 11 different hand movements. The classification results of artificial neural networks (ANN) were compared with those of linear discriminant analysis (LDA) for both datasets separately. For dataset 1, the mean classification accuracy of LDA obtained was 85.41% while ANN showed 91.14% accuracy. Similarly, for dataset 2, the mean classification accuracy achieved with LDA was 93.54% while with ANN, it was 97.69%. Besides, p-values were determined for both datasets which revealed better classification results of ANN as compared to LDA. The overall results of this study show that ANN performed better classification and recognition of hand movements as compared to LDA. The findings of this study offer important insights regarding the selection of classifiers of EMG signals which are critical to evaluating the accurate performance of prosthetic human organs.

Robust Simultaneous and Proportional Myoelectric Control Scheme for Individuals with Transradial Amputations.

Commercial myoelectric prostheses need to be accurate and clinically viable to be successful. This study proposed a simultaneous and proportional control scheme with frequency division technique (SPEC-FDT) to address limitations in current myoelectric prosthesis control, specifically to address non-stationaries such as contraction level variations and unintended activations. Twenty able-bodied participants (14 males and 6 females, age 23.4 ± 3.0) and four individuals with transradial amputations performed wrist movements (flexion/extension, rotations and combined movements) in two degrees-of freedom virtual tasks. The SPEC-FDT had a completion rate (CR)>90% for both control and clinical participants which was significantly higher than the conventional technique (CR=68%). Our results showed that SPEC-FDT is highly accurate for both able-bodied and clinical participants and provides a robust myoelectric control scheme allowing for increased prosthetic hand functions.

Forearm High-Density Electromyography Data Visualization and Classification with Machine Learning for Hand Prosthesis Control.

Electromyography offers a way to interface an amputee's resilient muscles to control a bionic prosthesis. While myoelectric prostheses are promising, user acceptance of these devices remain low due to a lack of intuitiveness and ease-of-use. Using a low-cost wearable flexible electrodes array, the proposed system leverages high-density surface electromyography (HD-EMG) and deep learning techniques to classify forearm muscle contractions. These techniques allow for increased intuitiveness and ease-of-use of a myoelectric control scheme with a single easy-to-install electrodes apparatus. This paper proposes a flexible electrodes array construction using standard printed circuit board manufacturing processes for low-cost and quick design-to-production cycles. HD-EMG dataset visualization with t-distributed Stochastic Neighbor Embedding (t-SNE) is introduced, and offline classification results of the wearable gesture recognition system for hand prosthesis control are validated on a group of 8 able-bodied subjects. Using a majority vote on 5 successive inferences, a median recognition accuracy of 98.61 % was obtained across the group for an 8 gestures set. For a 6 gestures set containing commonly used prosthesis positions, the median accuracy reached 99.57 % with the majority vote.

Bilateral symmetry in ankle-muscle activation in transtibial amputees.

By 2020, over 2.2 million people in the United States will be living with an amputated lower limb. The functional impact of amputations presents significant challenges in daily living activities. While significant work has been done to develop smart prosthetics, for the long-term development of effective and robust myoelectric control systems for transtibial amputees, there is still much that needs to be understood regarding how extrinsic muscles of the lower limb are utilized post-amputation. In this study, we examined muscle activity between the intact and residual limbs of three transtibial amputees with the aim of identifying differences in voluntary recruitment patterns during a bilateral motor task. We report that while there is variability across subjects, there are consistencies in the muscle recruitment patterns for the same functional movement between the intact and the residual limb within each subject. These results provide insights for how symmetric activation in residual muscles can be characterized and used to develop myoelectric control strategies for prosthetic devices in transtibial amputees.

Linear regression with frequency division technique for robust simultaneous and proportional myoelectric control during medium and high contraction-level variation

Myoelectric controlled prostheses systems have evolved significantly over the last few decades, however, there remains a need for more robust systems. Clinically, prosthesis control schemes must be robust to various Electromyography (EMG) signal non-stationarities such as unintended activations and contraction level variations to ensure appropriate operation of the prosthesis. This study compared performance measures between two control schemes, linear regression with frequency division technique (LR-FDT) and standard bandpass processing (LR-Bandpass) for two contraction levels (medium and high) to investigate robustness to EMG non-stationarities. Twenty able-bodied control (14 males and 6 females, age 23.4 ± 3.0) and four individuals with trans-radial amputations performed wrist movements (flexion/extension, rotations and combined movements) in two degrees-of-freedom (DOF) virtual tasks. For control participants, LR-FDT had a mean completion rate (CR) of 95.33%, which was significantly higher than LR-Bandpass with a CR of 64.08% (p<0.001). The clinical participants showed a CR>90% using LR-FDT and had an average CR of 69.8% using LR-Bandpass. LR-FDT method performed significantly better in all other performance indices in at least one movement type. There was no significant difference in the performance of LR-FDT between medium and high contraction levels. This study showed that LR-FDT is advantageous in online myoelectric control as it introduces a more accurate, robust and contraction level invariant control scheme for performing prosthetic hand movements. This study is the first to use frequency-based features with a simultaneous and proportional myoelectric control (SPEC) scheme.

A Modular Adjustable Transhumeral Prosthetic Socket for Evaluating Myoelectric Control.

Novel myoelectric control strategies may yield more robust, capable prostheses which improve quality of life for those affected by upper-limb loss; however, the development and translation of such strategies from an experimental setting towards daily use by persons with limb loss is a slow and costly process. Since prosthesis functionality is highly dependent on the physical interface between the user’s prosthetic socket and residual limb, assessment of such controllers under realistic (noisy) environmental conditions, integrated into prosthetic sockets, and with participants with amputation is essential for obtaining representative results. Unfortunately, this step is particularly difficult as participant- and control strategy-specific prosthetic sockets must be custom-designed and manufactured. There is thus a need for a system to reduce these burdens and facilitate this crucial phase of the development pipeline. This study aims to address this gap through the design and assessment of an inexpensive and easy-to-use 3D-printed Modular-Adjustable transhumeral Prosthetic Socket (MAPS). This 3D-printed, open-source socket was developed in consultation with prosthetists and compared with a participant-specific suction socket in a single-participant case-study. We conducted mechanical and functional assessments to ensure that the developed socket enabled similar performance compared to participant-specific sockets. Both socket systems yielded similar results in mechanical and functional assessments, as well as in self-reported user feedback. The MAPS system shows promise as a research tool which catalyzes the development and deployment of novel myoelectric control strategies by better-enabling comprehensive assessment involving participants with amputations.

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.