Electromyogram (EMG)-based control interfaces are increasingly used in robot teleoperation, prosthetic devices control and also in controlling robotic exoskeletons. Over the last two decades researchers have come up with a plethora of decoding functions to map myoelectric signals to robot motions. However, this requires a lot of training and validation data sets, while the parameters of the decoding function are specific for each subject. In this thesis we propose a new methodology that doesn't require training and is not user-specific. The main idea is to supplement the decoding functional error with the human ability to learn inverse model of an arbitrary mapping function. We have shown that the subjects gradually learned the control strategy and their learning rates improved. We also worked on identifying an optimized control scheme that would be even more effective and easy to learn for the subjects. Optimization was done by taking into account that muscles act in synergies while performing a motion task. The low-dimensional representation of the neural activity was used to control a two-dimensional task. Results showed that in the case of reduced dimensionality mapping, the subjects were able to learn to control the device in a slower pace, however they were able to reach and retain the same level of controllability. To summarize, we were able to build an EMG-based controller for robot devices that would work for any subject, without any training or decoding function, suggesting human-embedded controllers for robotic devices.
In recent years, the requirements of individual assistant systems for elderly and disabled people are daily increasing, as well as the function expansion of prosthetic control, military, residential and commercial robots. In this case, human-robot interactions have become a popular research area. Since these robots are directly interacted with the users, there are several challenges in the design and control of such human-robot interaction technology. Electromyography (EMG) signal is the electrical signals of the human body, which contains a wealth of information on human action and can be used to determine the user's intent. The purpose of this thesis is to develop an EMG-based human-robot interface, which can identify the body's response by signal processing and model calculations, and can also transform the response into the motion control instructions, and control the robot to complete the body movement intentions. The existing physiological models have provided a continuous motion prediction method. This method of the 'simplified musculoskeletal model' took the mechanical revolute instead of human joint, the straight line instead of skeleton, and the straight segment between the muscle starting point and adhesion point instead of the muscle. During the complex motion of human body, the prediction accuracy of this model is greatly reduced since it is not close to the human actual physiological structure. Also, it cannot be used for the calculation when the muscular force line crosses the joint center. Currently, the studies of the impact of physiological model parameters to the sensitivity of interface have three problems: the amount of assessed parameters was few, the evaluation method was single, and the results of different researches had disagreement. Especially, the analysis of overall parameters in the neuromuscular model was less. The existing sensitivity evaluation was focused on the impact of musculotendon parameters sensitivity to the model. Through two cases study of elbow flexion/extension and forearm pronation/supination, this thesis overviews the new progresses that aim to address the existing gaps in this research field. The elbow joint was selected to implement a new method of muscle modeling, which could improve the accuracy of model during the complex motion of the elbow, while ensuring the real-time processing of the interface. The forearm rotation was chosen because of the weak EMG of forearm muscles, the short moving time and small changes in muscle length. The interface for forearm rotation has its particularity. A new EMG-driven elbow physiological model has been developed to predict the elbow flexion and extension. In the process of modeling, this thesis made assumptions based on the physiological properties of muscle. Through the elbow experiments from a plurality of subjects and a variety of movements, the model’s ability of accurately predicting different moving trajectories was verified. The model was also implemented and verified by a single degree of freedom (DOF) exoskeleton. A new EMG-driven physiological model for forearm pronation/supination has been established. It can predict the forearm continuous rotation movement by the EMG activations from the superficial part of three muscles. The model contained a unique physiology musculoskeletal model. The experiments from four subjects showed the effectiveness of this method. The establishment of this forearm physiological model has opened up a new way for the prediction of complex joint system with small amplitude motions. A new sensitivity assessment method of model parameters, three-step layered approach, has been established. By using this method, this thesis analyzed the characteristics of the model parameters. A relatively small subset of the parameters was generated for parameter tuning. This method provided a new way of thinking for the parameters sensitivity analysis. The purpose of parameter tuning is to make the model can precisely match every subject. This thesis programmed two kinds of evolutionary algorithm - Differential Evolution (DE) and Genetic Algorithm (GA), and experimentally compared their performances in three aspects. Because of the high accuracy and fast convergence capability, DE can be used for fast online tuning. And GA can only be used in offline tuning. A controller based on the fusion of EMG and force information has been proposed to validate the proposed models in real time control environment. A 5-DOF upper limb exoskeleton was developed by the Medical and Rehabilitation Research Group at the University of Auckland, the exoskeleton was used to evaluate the effectiveness of the EMG based controller (EBC). The results showed that the dynamic auxiliary effect of the exoskeleton is obvious (the decrease of muscle activation could be ensured above 52% when the assistance works), and the physiological model based EBC can adapt to different individuals. This also showed the effectiveness and online adaptability of the EMG-based Neuromuscular Interface proposed by this thesis.
Utilization of electrodiagnosis; namely electromyography (EMG), nerve conduction studies, late responses, repetitive nerve stimulation techniques, quantitative EMG and evoked potentials, has long been discussed in many text books as basic principles. However the usage of electroneuromyography is rather new in some aspects when compared with tasks of daily practise. This book, we believe, will cover and enlighten those aspects where electrodiagnosis has begun to play important roles nowadays.
This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphsis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on man-machine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level.
Through expanded intelligence, the use of robotics has fundamentally transformed a variety of fields, including manufacturing, aerospace, medicine, social services, and agriculture. Continued research on robotic design is critical to solving various dynamic obstacles individuals, enterprises, and humanity at large face on a daily basis. Robotic Systems: Concepts, Methodologies, Tools, and Applications is a vital reference source that delves into the current issues, methodologies, and trends relating to advanced robotic technology in the modern world. Highlighting a range of topics such as mechatronics, cybernetics, and human-computer interaction, this multi-volume book is ideally designed for robotics engineers, mechanical engineers, robotics technicians, operators, software engineers, designers, programmers, industry professionals, researchers, students, academicians, and computer practitioners seeking current research on developing innovative ideas for intelligent and autonomous robotics systems.
Modern robotic prosthetic devices for upper limb amputees promise to alleviate an important disability, but are underutilized due to the inability to properly control them. Specifically, the devices afford more degrees of freedom (DOFs) than are controllable by easily decoded biological signals. These devices, such as the DEKA arm, can have as many as 18 DOFs, although six is a more typical number (control of each finger plus thumb rotation). Unfortunately, the use of these devices remains limited by the ability of users to simultaneously control more than one degree of freedom at a time with commercially deployed technology. Control of robotic prosthetic devices is typically achieved through electromyogram (EMG) signals read from the residual limb. While several groups have reported being able to use multiple EMG sensors to classify the user intent from residual muscle activity, such systems have not proven robust enough to translate to clinical use and are not intuitive. In the first part of this research, the prosthetic control problem is re-framed as a Human Robot Interface problem, developing and clinically evaluating several robotic interface methods which can eliminate or complement the use of EMG signals while allowing the user to quickly achieve more grasping patterns, thus allowing the use of all the DOFs available in the prosthetic device. Three healthy limb based methods have been developed and evaluated, including: 1) the use of the healthy hand to teleoperate the proshtetic device via a Mirroring Glove, 2) the use of the healthy hand to issue pre-programmed commands to the prosthetic device via a Gesture Glove and 3) the use of the healthy hand with extremely light fingernail worn devices to issue commands to the prosthetic device. In the second part of this research, a field-deployable and easy way of training a multiple input based EMG classifier is presented and extended to using Force Myography (FMG) data fused with EMG data. Overall, a number of different experiments were conducted with a total of 20 human subjects, including 2 amputees, and the following conclusions were reached: 1) Healthy limb based prosthetic device control can match the performance speed of EMG based control with very little training 2) Gesture based control of the healthy limb is faster than mirrored teleoperation except in the case of tasks which are mirrored by their nature 3) Bilateral hand movements combined with kinematic tracking of the healthy limb can be utilized to train a Force Myography (FMG) based classifier as well as an EMG based classifier, and that the combination of the two modalities hold promise to make a readily deployable multi-DOF EMG/FMG classifier system a reality.
