Panagiotis K. Artemiadis

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.

An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features

Human-robot control interfaces have received increased attention during the past decades. With the introduction of robots in everyday life, especially in providing services to people with special needs (i.e., elderly, people with impairments, or people with disabilities), there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the users skin, making the users upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real time an anthropomorphic robot arm in 3-D space, using upper limb motion estimates based only on EMG recordings. Moreover, the proposed interface is robust to EMG changes with respect to time, mainly caused by muscle fatigue or adjustments of contraction level. The efficiency of the method is assessed through real-time experiments, including random arm motions in the 3-D space with variable hand speed profiles.

A Switching Regime Model for the EMG-Based Control of a Robot Arm

Human-robot control interfaces have received increased attention during the last decades. These interfaces increasingly use signals coming directly from humans since there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from the muscles of the human upper limb are used as the control interface between the user and a robot arm. A switching regime model is used to decode the EMG activity of 11 muscles to a continuous representation of arm motion in the 3-D space. The switching regime model is used to overcome the main difficulties of the EMG-based control systems, i.e., the nonlinearity of the relationship between the EMG recordings and the arm motion, as well as the nonstationarity of EMG signals with respect to time. The proposed interface allows the user to control in real time an anthropomorphic robot arm in the 3-D space. The efficiency of the method is assessed through real-time experiments of four persons performing random arm motions.

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.

A biomimetic approach to inverse kinematics for a redundant robot arm

Redundant robots have received increased attention during the last decades, since they provide solutions to problems investigated for years in the robotic community, e.g. task-space tracking, obstacle avoidance etc. However, robot redundancy may arise problems of kinematic control, since robot joint motion is not uniquely determined. In this paper, a biomimetic approach is proposed for solving the problem of redundancy resolution. First, the kinematics of the human upper limb while performing random arm motion are investigated and modeled. The dependencies among the human joint angles are described using a Bayesian network. Then, an objective function, built using this model, is used in a closed-loop inverse kinematic algorithm for a redundant robot arm. Using this algorithm, the robot arm end-effector can be positioned in the three dimensional (3D) space using human-like joint configurations. Through real experiments using an anthropomorphic robot arm, it is proved that the proposed algorithm is computationally fast, while it results to human-like configurations compared to previously proposed inverse kinematics algorithms. The latter makes the proposed algorithm a strong candidate for applications where anthropomorphism is required, e.g. in humanoids or generally in cases where robotic arms interact with humans.

Modeling, full identification and control of the mitsubishi PA-10 robot arm

This paper presents the modeling, identification and control of the 7 degrees of freedom (DoFs) Mitsubishi PA-10 robot arm. The backdrivability, high accuracy positioning capabilities and zero backlash afforded by its harmonic drive transmission, make the PA-10 ideal for precise manipulation tasks. However, the lack of any technical knowledge on the dynamic parameters of its links and the non linear characteristics of friction at its joints, make the development of an accurate dynamic model of the robot extremely challenging. The innovation of this research focuses on the development of the full dynamic model of the PA-10 robot arm, the development of a new non linear model for the friction at its joints, the estimation of the stiffness characteristics of its joints and finally the full identification of the dynamic parameters of the robot arm. The accuracy of the full dynamic model identified is proved by an end-effector trajectory tracking task using a model-based inverse dynamic controller.

EMG-based teleoperation of a robot arm in planar catching movements using ARMAX model and trajectory monitoring techniques

This paper presents a methodology of teleoperating a robot arm, using electromyographic (EMG) signals and a trajectory monitoring technique based on human motion analysis. EMG signals from the flexor and extensor muscles of the elbow joint are used to predict the human elbow joint angle, using an auto-regressive moving average with exogenous output (ARMAX) model. A position tracker is attached in the user upper arm, before the elbow joint. It has been identified from previous works on human physiology that the trajectory of the human hand during planar catching tasks lays on a straight line. This motion law is used in order to monitor and refine the trajectory of the human hand that is predicted through EMG and the ARMAX model. The experimental results show that the ARMAX model estimation for the elbow angle, in conjunction with the trajectory monitoring technique, is able to predict the user motion with high accuracy, within different target points unknown to the system, and various hand velocities

EMG-based position and force control of a robot arm: Application to teleoperation and orthosis

This paper presents a methodology for the control of a robot arm, using electromyographic (EMG) signals. EMG signals from the muscles of the shoulder and elbow joints are used to predict the corresponding joint angles and the force exerted by the user to the environment through his/her forearm. The users motion is restricted to a plane. An analysis of various parametric models is carried out in order to define the appropriate form of the model to be used for the EMG-based estimates of the motion and force exerted by the user. A multi-input multi-output (MIMO) black-box state-space model is found to be the most accurate and is used to predict the joint angles and the force exerted during motion, in high frequency. A position tracking system is used to track the shoulder and elbow joint angles in low frequency to avoid drifting phenomena in the joints estimates. The high frequency model estimates, the low-frequency position tracker and a Kalman filter are used to control a torque controlled robot arm in the frequency of 500 Hz. The proposed system is tested both on teleoperation and orthosis scenarios. The experimental results prove the high accuracy of the system within a variety of motion profiles.

Learning human reach-to-grasp strategies: Towards EMG-based control of robotic arm-hand systems

Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the musculoskeletal system of the upper limbs. In this paper, we study the muscular co-activation patterns during a variety of reach-to-grasp motions, and we introduce a learning scheme that can discriminate between different strategies. This scheme can then classify reach-to-grasp strategies based on the muscular co-activations. We consider the arm and hand as a whole system, therefore we use surface ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for task-specific motion and force estimation models, improving EMG-based control of robotic arm-hand systems.

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.