Mark Ison

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization

Proportional myoelectric control has been proposed for user-friendly interaction with prostheses, orthoses, and new human-machine interfaces. Recent research has stressed intuitive controls that mimic human intentions. However, these controls have limited accuracy and functionality, resulting in user-specific decoders with upper-bound constraints on performance. Thus, myoelectric controls have yet to realize their potential as a natural interface between humans and multifunctional robotic controls. This study supports a shift in myoelectric control schemes toward proportional simultaneous controls learned through the development of unique muscle synergies. A multiple day study reveals natural emergence of a new muscle synergy space as subjects identify the system dynamics of a myoelectric interface. These synergies correlate with long-term learning, increasing performance over consecutive days. Synergies are maintained after one week, helping subjects retain efficient control and generalize performance to new tasks. The extension to robot control is also demonstrated with a robot arm performing reach-to-grasp tasks in a plane. The ability to enhance, retain, and generalize control, without needing to recalibrate or retrain the system, supports control schemes promoting synergy development, not necessarily user-specific decoders trained on a subset of existing synergies, for efficient myoelectric interfaces designed for long-term use.

High-Density Electromyography and Motor Skill Learning for Robust Long-Term Control of a 7-DoF Robot Arm

Myoelectric control offers a direct interface between human intent and various robotic applications through recorded muscle activity. Traditional control schemes realize this interface through direct mapping or pattern recognition techniques. The former approach provides reliable control at the expense of functionality, while the latter increases functionality at the expense of long-term reliability. An alternative approach, using concepts of motor learning, provides session-independent simultaneous control, but previously relied on consistent electrode placement over biomechanically independent muscles. This paper extends the functionality and practicality of the motor learning-based approach, using high-density electrode grids and muscle synergy-inspired decomposition to generate control inputs with reduced constraints on electrode placement. The method is demonstrated via real-time simultaneous and proportional control of a 4-DoF myoelectric interface over multiple days. Subjects showed learning trends consistent with typical motor skill learning without requiring any retraining or recalibration between sessions. Moreover, they adjusted to physical constraints of a robot arm after learning the control in a constraint-free virtual interface, demonstrating robust control as they performed precision tasks. The results demonstrate the efficacy of the proposed man-machine interface as a viable alternative to conventional control schemes for myoelectric interfaces designed for long-term use.

Embedded Human Control of Robots Using Myoelectric Interfaces

Myoelectric controlled interfaces have become a research interest for use in advanced prostheses, exoskeletons, and robot teleoperation. Current research focuses on improving a users initial performance, either by training a decoding function for a specific user or implementing “intuitive” mapping functions as decoders. However, both approaches are limiting, with the former being subject specific, and the latter task specific. This paper proposes a paradigm shift on myoelectric interfaces by embedding the human as controller of the system to be operated. Using abstract mapping functions between myoelectric activity and control actions for a task, this study shows that human subjects are able to control an artificial system with increasing efficiency by just learning how to control it. The method efficacy is tested by using two different control tasks and four different abstract mappings relating upper limb muscle activity to control actions for those tasks. The results show that all subjects were able to learn the mappings and improve their performance over time. More interestingly, a chronological evaluation across trials reveals that the learning curves transfer across subsequent trials having the same mapping, independent of the tasks to be executed. This implies that new muscle synergies are developed and refined relative to the mapping used by the control task, suggesting that maximal performance may be achieved by learning a constant, arbitrary mapping function rather than dynamic subject- or task-specific functions. Moreover, the results indicate that the method may extend to the neural control of any device or robot, without limitations for anthropomorphism or human-related counterparts.

Learning efficient control of robots using myoelectric interfaces

Myoelectric controlled interfaces are a vital component for advancing applications in prostheses, exoskeletons, and robot teleoperation. Current methods search for optimal neural decoders for enhanced initial user performance. However, recent studies demonstrate learning an inverse model of abstract decoders to improve performance over time. This paper proposes a paradigm shift on myoelectric interfaces by embedding the human as controller of a system and allowing the human to learn how to control it via control tasks with similar mapping functions. The method is tested using two different control tasks and four different abstract mappings of upper limb myoelectric signals to control actions for those tasks. The results confirm that all subjects are able to learn the mappings and improve performance efficiency over time. A cross-trial evaluation reveals a significant learning transfer when a new control task is presented using the same mapping as a previous task, resulting in enhanced initial performance with the new task. Comparison of EMG signal evolution across subjects indicates a significant population-wide muscle synergy development that results from learning and implementing the inverse model of the mapping function to complete the tasks. This suggests that efficient performance may be achieved by learning a constant, arbitrary mapping function applied to multiple control tasks rather than dynamic subject- or task-specific functions. Moreover, this method can be used for the neural control of any device or robot, without restricting them to anthropomorphic or human-related counterparts.

User-Independent Hand Motion Classification With Electromyography

Electromyographic (EMG) processing is an important research area with direct applications to prosthetics, exoskeletons and human-machine interaction. Current state of the art decoding methods require intensive training on a single user before it can be utilized, and have been unable to achieve both user-independence and real-time performance. This paper presents a real-time EMG classification method which generalizes across users without requiring an additional training phase. An EMG-embedded sleeve quickly positions and records from EMG surface electrodes on six forearm muscles. An optimized decision tree classifies signals from these sensors into five distinct movements for any given user using EMG energy synergies between muscles. This method was tested on 10 healthy subjects using leave-one-out validation, resulting in an overall accuracy of 79±6.6%, with sensitivity and specificity averaging 66% and 97.6%, respectively, over all classified motions. The high specificity values demonstrate the ability to generalize across users, presenting opportunities for large-scale studies and broader accessibility to EMG-driven applications.Copyright

Beyond User-Specificity for EMG Decoding Using Multiresolution Muscle Synergy Analysis

Electromyographic (EMG) processing is a vital step towards converting noisy muscle activation signals into robust features that can be decoded and applied to applications such as prosthetics, exoskeletons, and human-machine interfaces. Current state of the art processing methods involve collecting a dense set of features which are sensitive to many of the intra- and inter-subject variability ubiquitous in EMG signals. As a result, state of the art decoding methods have been unable to obtain subject independence. This paper presents a novel multiresolution muscle synergy (MRMS) feature extraction technique which represents a set of EMG signals in a sparse domain robust to the inherent variability of EMG signals. The robust features, which can be extracted in real time, are used to train a neural network and demonstrate a highly accurate and user-independent classifier. Leave-one-out validation testing achieves mean accuracy of 81.9±3.9% and area under the receiver operating characteristic curve (AUC), a measure of overall classifier performance over all possible thresholds, of 92.4±8.9%. The results show the ability of sparse MRMS features to achieve subject independence in decoders, providing opportunities for large-scale studies and more robust EMG-driven applications.Copyright

Simultaneous myoelectric control of a robot arm using muscle synergy-inspired inputs from high-density electrode grids

Myoelectric control has seen decades of research as a potential interface between human and machines. High-density surface electromyography (HDsEMG) non-invasively provides a rich set of signals representing underlying muscle contractions and, at a higher level, human motion intent. Many pattern recognition techniques have been proposed to predict motions based on these signals. However, control schemes incorporating pattern recognition struggle with long-term reliability due to signal stochasticity and transient changes. This study proposes an alternative approach for HDsEMG-based interfaces using concepts of motor skill learning and muscle synergies to address long-term reliability. Muscle synergy-inspired decomposition reduces HDsEMG into control inputs robust to small electrode displacements. The novel control scheme provides simultaneous and proportional control, and is learned by the subject simply by interacting with the device. In a multiple-day experiment, subjects learned to control a virtual 7-DoF myoelectric interface, displaying performance learning curves consistent with motor skill learning. On a separate day, subjects intuitively transferred this learning to demonstrate precision tasks with a 7-DoF robot arm, without requiring any recalibration. These results suggest that the proposed method may be a practical alternative to pattern recognition-based control for long-term use of myoelectric interfaces.

Enhancing practical multifunctional myoelectric applications through implicit motor control training systems

Despite holding promise for advances in prostheses and robot teleoperation, myoelectric controlled interfaces have had limited impact in commercial applications. Simultaneous multifunctional controls are desired, but often lead to frustration by users who cannot easily control the devices using state-of-the-art control schemes. This paper proposes and validates the use of implicit motor control training systems (IM-CTS) to achieve practical implementations of multifunctional myoelectric applications. Subjects implicitly develop muscle synergies needed to control a robotic application through an analogous visual interface without the associated physical constraints which may hinder learning. The learning then naturally transfers to perceived intuitive and robust control of the robotic device. The efficacy of the method is tested by comparing performance between two groups learning controls implicitly via the visual interface and explicitly via the robotic interface, respectively. The groups achieved comparable performance when performing tasks with the robotic device a week later. Moreover, the initial performance of the experimental group was significantly better than the control group achieved after up to 75 minutes of training. These findings support the use of IMCTS to achieve practical multifunctional control of a wide range of myoelectric applications without limiting them to intuitive mappings nor anthropomorphic devices.