Towards an Effective EMG-based Neuromuscular Interface for Human-robot Interaction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Towards an Effective EMG-based Neuromuscular Interface for Human-robot Interaction write by Ran Tao. This book was released on 2016. Towards an Effective EMG-based Neuromuscular Interface for Human-robot Interaction available in PDF, EPUB and Kindle. 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.
EMG-based Robot Control Interfaces
EMG-based Robot Control Interfaces - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook EMG-based Robot Control Interfaces write by Chris Wilson Antuvan. This book was released on 2013. EMG-based Robot Control Interfaces available in PDF, EPUB and Kindle. 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.
A Physiological Model Driven Neuromuscular Interface for Exoskeleton Assisted Rehabilitation
A Physiological Model Driven Neuromuscular Interface for Exoskeleton Assisted Rehabilitation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Physiological Model Driven Neuromuscular Interface for Exoskeleton Assisted Rehabilitation write by James W. L. Pau. This book was released on 2013. A Physiological Model Driven Neuromuscular Interface for Exoskeleton Assisted Rehabilitation available in PDF, EPUB and Kindle. Exoskeletons are anthropomorphic robotic devices that move in concert with their users to enhance their power and endurance capabilities. They are actively involved in exercising, reinforcing and moving patient limbs for rehabilitation or to assist in activities of daily living. However, exoskeletons were originally designed for fully-abled users and patients have difficulty controlling exoskeletons because they lack the physical capability. Therefore, an interface that can determine the user's intent to move is needed and this thesis aims to create such an interface with the electromyography (EMG) signal. The EMG signal results from the electrical activity within muscle and can be measured from the skin surface, without physical movement. A neuromuscular interface (NI), is defined as a module that consists of all the hardware and software components required to obtain, filter, process and output predictions of intended joint movement based on EMG signals. Existing interfacing methods based on the EMG signal are currently dependent on pattern recognition techniques that are only able to identify discrete states of movement. Physiological model-based approaches offer a means of continuous movement prediction, but these methods commonly require additional mass to enhance the EMG signals, the use of additional sensor data, or the simplification of movements beyond functional practicality. The approaches are also centred around hinge joints or joints that have been simplified to a single degree of freedom (DOF). There have been few attempts to address the complexities of multiple DOF joint systems. Developing a model for multiple DOF joints requires a better understanding of the superficial muscles that actuate the joint because these limit the accessibility of EMG signal sites. Therefore, bettering the understanding of the relationship between the superficial muscles of a joint and the available DOFs is crucial for effective EMG-driven multiple DOF model development. Commonly used tuning methods and performance measures are also inadequate for EMG signal-based interfaces because the criteria for optimisation is not suited for movement prediction, and the best response does not always coincide with the most desirable response. This thesis outlines the developments that aim to address these gaps through two case studies: the elbow joint and the masticatory system. The elbow joint was selected because of its simplicity and the masticatory system was selected because it contains a highly complex joint system and a challenging structure. An EMG-driven physiological model was initially developed for the elbow joint. It uses only EMG signals and a unique neuromusculoskeletal structure to predict the continuous motion of the elbow joint. With tuning, the model was able to identify random movements from multiple subjects in offline analyses. The model was also realised as a physical interface and the concept of an NI was tested and proved in real-time through the control of a physical representation of the elbow joint. A new EMG-driven physiological model for the masticatory system was built that was able to accommodate two DOFs of mandibular motion. The determination of EMG channels and the appropriate DOFs was done through a specifically designed study to better understand the influences and characteristics of the superficial mandibular muscles. This resulted in a unique muscular arrangement and a model of the masticatory system that was capable of movement in the vertical and lateral directions. The physiological model of the masticatory system was also combined with an artificial neural network (ANN) to form a hybrid model. This addresses an identified problem with multiple DOF models that concern the multiple roles a single muscle plays in different movements. The purpose of the ANN was to identify the type of movement occurring and then enhance or suppress the output from the physiological model. This new dynamism allowed continuous movement prediction with less interference between the available DOFs. The physiological models were all validated experimentally using data from multiple subjects. However, existing tuning approaches were found to be insufficient and would produce results that were not always in the best interests of the intended application. Thus, a serial tuning approach was proposed that utilised a different performance measure, the correlation coefficient, in a sequential optimisation process. This new approach produced better movement trajectories than current popular methods. These contributions have led to three journal articles, three conference papers, one provisional patent, and have laid substantial groundwork for the development of effective physiological models and NIs. Future work involves refining and improving the modelling process, and beginning to focus on the hardware components to complete the realisation of an NI.
Muscular Pattern Based on Multichannel Surface EMG During Voluntary Contractions of the Upper-limb
Muscular Pattern Based on Multichannel Surface EMG During Voluntary Contractions of the Upper-limb - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Muscular Pattern Based on Multichannel Surface EMG During Voluntary Contractions of the Upper-limb write by Mislav Jordanić. This book was released on 2018. Muscular Pattern Based on Multichannel Surface EMG During Voluntary Contractions of the Upper-limb available in PDF, EPUB and Kindle. Extraction of neuromuscular information is an important and extensively researched issue in biomedical engineering. lnformation on muscle control can be used in numerous human-machine interfaces and control applications, including rehabilitation engineering, e.g., prosthetics, exoskeletons and rehabilitation robots. Neuromuscular information can be extracted at the brain level, peripheral nerves, or muscles. Among these options, muscle interface is the only viable way of information extraction in everyday life. Although brain and nerve recordings are promising,they usually require invasive measurement and achieve relatively low extraction speed which prevents real time control. But neuromuscular information can also be inferred from recorded electrical activity generated by contracting muscle(electromyography, EMG). Even though in EMG recordings information is not obtained directly from neural cells, it contains similar information as nerve recording. Given the fact that motoneuron induces action potentials of muscle fibers, information extracted from EMG is equivalent to information extracted from corresponding motor neurons . Moreover, muscles contain multiple motor units that activate simultaneously so their electrical activity sums on the surface oft he skin, resulting in a relatively high amplitude compared to the other bioelectrical signals. Therefore, due to the richness of neural information,non invasiveness and high signal-to-noise ratio, the surface EMG is extensively used for man-machine interfacing, especially in commercial/clinical upper-limb prosthetic control.Motivation and merit of this thesis lies in the fact that information associated with muscular pattern during exercises can be very useful in differenta pplications such as monitoring patients' control strategies during recovery, personalizing rehabilitation processes to increase their effectiveness or to provide information to be used for control of external devices(EMG based control of prosthesis orexoskeletons).Within this Doctoral thesis a pattern recognition approach was used to assess neuromuscular information and to identify subjects' intended motion based on multichannel surface electromyographic recordings. Research was focused on control strategies of upper-limb, both in normal subjects and in patients with impaired mobility caused by incomplete spinal cord injury. Methods which are proposed can be used for the design and monitoring of rehabilitation therapies intended for patients with neuromuscular impairment, as well for the control of external devices like rehabilitation robots, exoskeletons, prostheses and even virtual games.
Biomechatronics in Medical Rehabilitation
Biomechatronics in Medical Rehabilitation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Biomechatronics in Medical Rehabilitation write by Shane (S.Q.) Xie. This book was released on 2017-01-28. Biomechatronics in Medical Rehabilitation available in PDF, EPUB and Kindle. This book focuses on the key technologies in developing biomechatronic systems for medical rehabilitation purposes. It includes a detailed analysis of biosignal processing, biomechanics modelling, neural and muscular interfaces, artificial actuators, robot-assisted training, clinical setup/implementation and rehabilitation robot control. Encompassing highly multidisciplinary themes in the engineering and medical fields, it presents researchers’ insights into the emerging technologies and developments that are being utilized in biomechatronics for medical purposes. Presenting a detailed analysis of five key areas in rehabilitation robotics: (i) biosignal processing; (ii) biomechanics modelling; (iii) neural and muscular interfaces; (iv) artificial actuators and devices; and (v) the use of neurological and muscular interfaces in rehabilitation robots control, the book describes the design of biomechatronic systems, the methods and control systems used and the implementation and testing in order to show how they fulfil the needs of that specific area of rehabilitation. Providing a comprehensive overview of the background of biomechatronics and details of new advances in the field, it is especially useful for researchers, academics and graduates new to the field of biomechatronics engineering, and is also of interest to researchers and clinicians in the medical field who are not engineers.
