Proportional Myoelectric Control Research Articles

Improving Complex Task Performance in Powered Upper Limb Exoskeletons With Adaptive Proportional Myoelectric Control for User Motor Strategy Tracking

Improving Complex Task Performance in Powered Upper Limb Exoskeletons With Adaptive Proportional Myoelectric Control for User Motor Strategy Tracking

Gait variability of outdoor vs treadmill walking with bilateral robotic ankle exoskeletons under proportional myoelectric control.

Lower limb robotic exoskeletons are often studied in the context of steady-state treadmill walking in laboratory environments. However, the end goal of these devices is often adoption into our everyday lives. To move outside of the laboratory, there is a need to study exoskeletons in real world, complex environments. One way to study the human-machine interaction is to look at how the exoskeleton affects the user's gait. In this study we assessed changes in gait spatiotemporal variability when using a robotic ankle exoskeleton under proportional myoelectric control both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would not lead to significant changes in variability inside on a treadmill or outside compared to not using the exoskeletons. In addition, we hypothesized that walking outside would lead to higher variability both with and without the exoskeletons compared to treadmill walking. In support of our hypothesis, we found significantly higher coefficients of variation of stride length, stance time, and swing time when walking outside both with and without the exoskeleton. We found a significantly higher variability when using the exoskeletons inside on the treadmill, but we did not see significantly higher variability when walking outside overground. The value of this study to the literature is that it emphasizes the importance of studying exoskeletons in the environment in which they are meant to be used. By looking at only indoor gait spatiotemporal measures, we may have assumed that the exoskeletons led to higher variability which may be unsafe for certain target populations. In the context of the literature, we show that variability due to robotic ankle exoskeletons under proportional myoelectric control does not elicit different changes in stride time variability than previously found in other daily living tasks (uneven terrain, load carriage, or cognitive tasks).

Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors.

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback

Most transradial prosthesis users with conventional “Sequential” myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: “Intuitive” control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and “Mapping” control (wrist flexion–extension and ulnar-radial deviation controlled virtual target left–right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being “matched,” and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4–7 vs 2 matches, p < 0.001) and throughput (average of 0.75–1.25 vs 0.4 bits/s, p < 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.

Random forest-based simultaneous and proportional myoelectric control system for finger movements

A classification scheme for myoelectric control systems (MCS) cannot mimic complex hand movements. This paper presents simultaneous and proportional MCS by estimating the angles of fourteen finger joints using time-domain feature extraction and random forest. The experimental results show that the best feature was the root mean square (RMS). Furthermore, the random forest attained an average coefficient of determination (R2) of 0.85 compared to other regressors which perform below 0.75. The ANOVA tests indicated that the performance of the proposed system was significantly different. Therefore, the proposed system will be the best option for real-time MCS applications in the future.

Ankle Exoskeleton Assistance can Affect Step Regulation during Self-Paced Walking.

Exoskeleton assistance can reduce metabolic cost and increase preferred walking speed in unimpaired and impaired groups, but individual outcomes are highly variable. Assistance may influence step regulation, leading to individual modulation of gait variability, energetic cost, and balance control. In this study, we aimed to understand the effects of a powered ankle exoskeleton on step regulation and its relationship to self-selected walking speed, cost of transport, and gait variability. We asked 12 unimpaired young adults to walk at their comfortable walking speed on a self-paced treadmill in their regular shoes, with the exoskeleton tracking zero torque, and in two trials using proportional myoelectric control. We measured preferred walking speed, cost of transport (COT), mean and standard deviation of gait parameters, (step length, step time, and step width) and computed long-term correlations via detrended fluctuation analysis (DFA). In all exoskeleton trials, subjects walked significantly slower than in their shoes. However, the COT was equivalent between shoes and both proportional myoelectric control trials. Subjects also increased medio-lateral balance control by increasing their mean step width and reducing both short-term variability and long-term auto-correlation for this parameter. In the second powered trial subjects returned to the levels of control over step width exhibited during regular shoe walking. During the unpowered condition subjects showed a significant association between step width regulation, walking speed, and COT. However, these parameters were not significantly associated when the assistance was turned on. Together, these results demonstrate that the response to assistance is closely related to the stepping strategy, especially in the initial stages of learning.

Preliminary Validation of Proportional Myoelectric Control of A Commercially Available Robotic Ankle Exoskeleton.

Proportional myoelectric control of robotic lower limb exoskeletons can increase the variability and adaptability of biomechanical behaviors for assisting human movement compared to traditional state-based control. Previous exoskeletons using proportional myoelectric control have relied on pneumatic actuators and been limited to laboratory use. We applied proportional myoelectric control to a robotic ankle exoskeleton using a brushless DC motor (Dephy) and enabled it to work in community settings. Benchtop testing verified electromechanical responses similar to biological values (electromechanical delay of 22 ms and time to peak activation of 123 ms). Four healthy participants trained for thirty minutes each using bilateral ankle exoskeletons. From minute one of powered walking to minute 30 of powered walking, peak soleus EMG reduced by 17.9% as they learned to walk with exoskeleton assistance. Our future work will extend the powered walking period, measure metabolic cost, and measure gait variability between participants using proportional myoelectric control on fully portable, electromechanical ankle exoskeletons.

Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control.

We show that state-of-the-art deep neural networks achieve superior results in regression-based multi-class proportional myoelectric hand prosthesis control than two common baseline approaches, and we analyze the neural network mapping to explain why this is the case. Feedforward neural networks and baseline systems are trained on an offline corpus of 11 able-bodied subjects and 4 prosthesis wearers, using the R2 score as metric. Analysis is performed using diverse qualitative and quantitative approaches, followed by a rigorous evaluation. Our best neural networks have at least three hidden layers with at least 128 neurons per layer; smaller architectures, as used by many prior studies, perform substantially worse. The key to good performance is to both optimally regress the target movement, and to suppress spurious movements. Due to the properties of the underlying data, this is impossible to achieve with linear methods, but can be attained with high exactness using sufficiently large neural networks. Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result.

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task-dimensionality. Here, the potential application of higher-order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of synergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist's Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factorisation methods. The extracted control signals were fed to a ridge regression to estimate the wrist's kinematics based on real glove data. The Coefficient of Determination (R2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.

Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

BackgroundProcessing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration.MethodsForearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session.ResultsMetric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p<0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s d) separating MRL from LDA ranging from left|dright|=0.62 to left|dright|=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission.ConclusionsThe results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative, and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies, but conventional CNN can only exploit spatial correlations. Considering that the long short-term memory (LSTM) neural network is able to capture long-term and nonlinear dynamics of time-series data, in this article, we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. First, CNN is utilized to extract deep features from the sEMG spectrum, and then, these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intrasession and intersession evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM, and several representative machine learning approaches, particularly when complex wrist movements are activated.

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.

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 dynamic model of hand movements for proportional myoelectric control of a hand prosthesis.

In myo-controlled prosthetic hands, surface electromyographic signals are used to operate the hand actuators. A pre-requisite for effective control is that the intended movement is decoded from muscle activity. Simpler approaches use pattern recognition techniques, which assume a finite set of possible actions. However, this leads to unnatural, discontinuous control. Proportional controllers do not require a finite set of actions to be specified in advance but are difficult to use, particularly with dexterous multi-fingered hands. Here we discuss a control module which continuously predicts the intended movements from recorded multi-channel electromyographic activity. The module can be seen as a (simplified) forward model of the dynamics of the intact hand. We describe a procedure for estimating model parameters from hand movement and muscle activity data, and discuss its application to the proportional myoelectric control of a prosthetic hand.

A high-density surface EMG framework for the study of motor neurons controlling the intrinsic and extrinsic muscles of the hand.

We propose a framework based on high-density surface electromyography (HD-sEMG) to identify the neural drive to muscles controlling the human hand. High-density (320 channels) sEMG signals were recorded concurrently from intrinsic (the four dorsal interossei and thenar) and extrinsic (forearm) hand muscles and then decomposed into the constituent trains of motor unit (MU) action potentials. The participants performed pinch tasks with simultaneous activation of the thumb and one of the other fingers with sinusoidal force variations. The common drive among MUs across different muscles was extracted via principal component analysis (PCA) of the smoothed MU discharge rates. The first principal component of the smoothed discharge rates of all identified motor neurons explained 48.7 ± 15.4% of the total variance across all pinching tasks, indicating a common neural input shared by different muscles of the forearm and the hand.. When considering only the MUs extracted from extrinsic and intrinsic muscles, the percent of variance explained was 48.3 ± 15.3% and 57.1 ± 15.5%, respectively. This framework is conceived to use motor neuron activity for a proportional myoelectric control and rehabilitation technologies. A wearable adaptation of the framework is proposed for future perspectives.

Proportional Myoelectric Control of a Virtual Inverted Pendulum Using Residual Antagonistic Muscles: Toward Voluntary Postural Control.

This paper aims to investigate whether transtibial amputees are capable of coordinating the descending neural commands to antagonistic residual ankle muscles for performing dynamic tasks that require continuous, precise control. To achieve this goal, we developed a virtual inverted pendulum that was inherently unstable and mimicked human-like dynamics in a standing posture. Balancing this dynamic system requires continuous inputs, proportional to electromyography (EMG) magnitudes recorded from (residual) tibialis anterior (TA) and lateral gastrocnemius muscles (GAS), respectively. The six able-bodied and six transtibial amputees were recruited and asked to balance the inverted pendulum for ten 90-s trials. The results showed that the amputees were capable of controlling this unstable dynamic system with a proportional myoelectric control; however, they underperformed the able-bodied subjects, who maintained the pendulum closer to center ( p = 0.041 ). Compared to the performance in the initial two trials, amputees improved the performance by significantly reducing the number of pendulum falls ( p = 0.0329 ) and sway size ( p = 0.048 ) in the final two trials. However, the amount of improvement varied across amputee subjects. Amputee subjects demonstrated different task adaptation strategies, including reduction of erroneous residual muscle contractions, development of an appropriate state-action (pendulum state-EMG activation) relationship for the task, and/or reduction of muscle control variability with the improved task performance efficiency (i.e., increased inactivity and sway minimization). The results suggest that after the training of transtibial amputees in coordinating antagonistic residual muscles in dynamic systems, it may be feasible to implement the proportional myoelectric control of the powered ankle prostheses in order to assist the postural control mechanisms, such as anticipatory and compensatory postural adjustments.

Proportional Myoelectric Control of a Powered Ankle Prosthesis for Postural Control under Expected Perturbation: A Pilot Study.

In this study we aimed to investigate the potential for antagonistic residual muscles to generate anticipatory and compensatory postural adjustments and their benefit to postural control with proportional myoelectric control of a powered ankle prosthesis. We conducted this investigation using a predictable pendulum drop task with a single transtibial amputee. In two individual testing sessions the participant used his prescribed passive device and a powered device with pneumatic artificial muscles actuated proportionally to the activation of residual Tibialis Anterior (TA) and Lateral Gastrocnemius (GAS) muscles. Results demonstrated the transtibial amputee generated activations from the residual TA significantly earlier in the powered condition $(p=\theta.\theta\theta 7)$. In the powered condition anticipatory center of pressure excursions were significantly higher $(p=\theta.\theta 17)$, and peak center of mass excursions were reduced $(p=\theta.\theta 21)$. Peak medio-lateral center of pressure excursions were also significantly less in the direction of the intact limb for the powered condition $(p\ =\ \theta.\theta\theta 3)$. The results from this pilot study demonstrate the promise for transtibial amputees to generate anticipatory postural adjustments as well as the potential improvement of stability under expected perturbations. This pilot study provides an initial basis for future study using proportional myoelectric control via antagonistic residual muscles for the control of posture under expected perturbations.

Design of a Pneumatic Actuated Ankle-Foot Orthosis which has Talocrural and Subtalar Joint.

As aging progresses, risk of falls in elderly people increases due to the decreasing the range of motion (RoM) of eversion and the weakening of the plantar flexor. Balance training is performed to reduce the risk of falls and powered ankle-foot orthosis (PAFO) can be utilized in this process. The elderly can conduct training on the narrow roads or on the rugged ground with assistance on rotation of eversion and propulsion by PAFO. However, existing PAFOs were developed just by considering the talocrural joint which make the rotation of sagittal plane of ankle which is called by plantar flexion and dorsiflexion. So, we developed the 2 Degree of Freedom (DoF) PAFO which has the Talocrural and Subtalar joints for frontal plane rotation which is called inversion and eversion. We established the coordinate system and calculated the spatial formulas for talocrural and subtalar joint based on anatomical data. Developed PAFO has the pneumatic artificial muscle (PAM) as an actuator which is controlled by solenoid valve with PWM methods, light-weight 3D printed body and knee orthosis for interfacing with wearer in three area which are thigh, shank and foot. The total weight of developed PAFO is 2. 14kg and it can be 1. 44kg without the weight of knee orthosis (0.7kg). Measurement sensors for PAFO were the absolute encoders to measure the angle of talocrural and subtalar joint, tensile loadcell to measure the magnitude of force from PAM, pressure sensor to measure the pressure of PAM and FSR sensor to judge the phase of the gait cycle. The results of the paper show the validation of design, kinematics and functional capacity for human subject experiments with proportional myoelectric control (PMc) of developed PAFO.

Voluntary Control of Residual Antagonistic Muscles in Transtibial Amputees: Reciprocal Activation, Coactivation, and Implications for Direct Neural Control of Powered Lower Limb Prostheses.

Residual ankle muscles (i.e., previously antagonistic ankle muscles) of transtibial amputees are a potential source for continuous feedforward control of powered ankle prostheses using proportional myoelectric control. The ability for transtibial amputees to use their residual ankle muscles for two control input degrees of freedom (i.e., two independent myoelectric control input sources) for direct neural control depends on the ability for amputees to generate varying magnitudes of reciprocal activation and coactivation using their residual ankle muscles, which is not well understood. In this paper, we aimed to fill this knowledge gap. We asked 12 transtibial amputees to control the 2-D movement of a computer cursor using continuous proportional myoelectric control via their residual plantar flexor and residual dorsiflexor muscles to define their reachable 2-D control input space. The x-y position of the computer cursor was directly proportional to the independent continuous myoelectric control signals from the residual lateral gastrocnemius (x-axis) and the residual tibialis anterior (y-axis) where the limits of each axis were 0%-100% maximum voluntary activation of the corresponding residual muscle. Our results show that the reachable control input space varied widely across amputee subjects ranging from 38% to 81% of the maximum possible control input space. The cumulative time for the amputee subjects to saturate their reachable control input space ranged from 1.95 to 6.85 min. The amputee subjects used different residual muscle activation patterns and coordination strategies to expand their reachable control input space depending on their ability to perform coactivation and reciprocal activation using their residual plantar flexor and dorsiflexor muscles. The future development of powered lower limb prostheses using direct continuous proportional myoelectric control via residual muscles (e.g., for direct voluntary control of prosthesis joint impedance) should consider how an amputee user's immediately accessible residual muscle activation patterns and reachable 2-D control input space may affect their learning and performance.

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.