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.
Towards an Effective EMG-based Neuromuscular Interface for Human-robot Interaction
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.
Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality
Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality - 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 Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality write by Yang Shen. This book was released on 2019. Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality available in PDF, EPUB and Kindle. Every year there are about 800,000 new stroke patients in the US, and many of them suffer from upper limb neuromuscular disabilities including but not limited to: weakness, spasticity and abnormal synergy. Patients usually have the potential to rehabilitate (to some extent) based on neuroplasticity, and physical therapy intervention helps accelerate the recovery. However, many patients could not afford the expensive physical therapy after the onset of stroke, and miss the opportunity to get recovered. Robot-assisted rehabilitation thus might be the solution, with the following unparalleled advantages: (1) 24/7 capability of human arm gravity compensation; (2) multi-joint movement coordination/correction, which could not be easily done by human physical therapists; (3) dual-arm training, either coupled in joint space or task space; (4) quantitative platform for giving instructions, providing assistance, exerting resistance, and collecting real-time data in kinematics, dynamics and biomechanics; (5) potential training protocol personalization; etc. However, in the rehabilitation robotics field, there are still many open problems. I am especially interested in: (1) compliant control, in high-dimensional multi-joint coordination condition; (2) assist-as-needed (AAN) control, in quantitative model-based approach and model-free approach; (3) dual-arm training, in both symmetric and asymmetric modes; (4) system integration, e.g., virtual reality (VR) serious games and graphical user interfaces (GUIs) design and development. Our dual-arm/hand robotic exoskeleton system, EXO-UL8, is in its 4th generation, with seven (7) arm degrees-of-freedom (DOFs) and one (1) DOF hand gripper enabling hand opening and closing on each side. While developing features on this research platform, I contributed to the robotics research field in the following aspects: (1) I designed and developed a series of eighteen (18) serious VR games and GUIs that could be used for interactive post-stroke rehabilitation training. The VR environment, together with the exoskeleton robot, provides patients and physical therapists a quantitative rehabilitation training platform with capability in real-time human performance data collection and analysis. (2) To provide better compliant control, my colleagues and I proposed and implemented two new admittance controllers, based on the work done by previous research group alumni. Both the hyper parameter-based and Kalman Filter-based admittance controllers have satisfactory heuristic performance, and the latter is more promising in future adaptation. Unlike many other upper-limb exoskeletons, our current system utilizes force and torque (F/T) sensors and position encoders only, no surface electromyography (sEMG) signals are used. It brings convenience to practical use, as well as technical challenges. (3) To provide better AAN control, which is still not well understood in the academia, I worked out a redundant version of modified dynamic manipulability ellipsoid (DME) model to propose an Arm Postural Stability Index (APSI) to quantify the difficulty heterogeneity of the 3D Cartesian workspace. The theoretical framework could be used to teach the exoskeleton where and when to provide assistance, and to guide the virtual reality where to add new minimal challenges to stroke patients. To the best of my knowledge, it is also for the first time that human arm redundancy resolution was investigated when arm gravity is considered. (4) For the first time, my colleagues and I have done a pilot study on asymmetric dual-arm training using the exoskeleton system on one (1) post-stroke patient. The exoskeleton on the healthy side could trigger assistance for that on the affected side, and validates that the current mechanism/control is eligible for asymmetric dual-arm training. (5) Other works of mine include: activities of daily living (ADLs) data visualization for VR game difficulty design; human arm synergy modeling; dual-arm manipulation taxonomy classification (on-going work).
Wearable Robotics
Wearable Robotics - 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 Wearable Robotics write by Jacob Rosen. This book was released on 2019-11-16. Wearable Robotics available in PDF, EPUB and Kindle. Wearable Robotics: Systems and Applications provides a comprehensive overview of the entire field of wearable robotics, including active orthotics (exoskeleton) and active prosthetics for the upper and lower limb and full body. In its two major sections, wearable robotics systems are described from both engineering perspectives and their application in medicine and industry. Systems and applications at various levels of the development cycle are presented, including those that are still under active research and development, systems that are under preliminary or full clinical trials, and those in commercialized products. This book is a great resource for anyone working in this field, including researchers, industry professionals and those who want to use it as a teaching mechanism. Provides a comprehensive overview of the entire field, with both engineering and medical perspectives Helps readers quickly and efficiently design and develop wearable robotics for healthcare applications
Advanced Methods and Applications for Neurointelligence
Advanced Methods and Applications for Neurointelligence - 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 Advanced Methods and Applications for Neurointelligence write by Manning Wang. This book was released on 2024-05-31. Advanced Methods and Applications for Neurointelligence available in PDF, EPUB and Kindle. Neurointelligence techniques play a key role in building general artificial intelligence systems. Some researchers and engineers have tried to design novel bio-inspired algorithms and hardware by mimicking the working principles of biological nervous systems. Benefiting from the progress in representational learning, neuroscience, and computational hardware, bio-inspired research has greatly contributed to the development of neurointelligence. Currently, advanced bio-inspired methods have been widely applied in robotics, visual scene understanding, medical image analysis, human-machine interaction and so on. Moreover, neurointelligence covers interdisciplinary topics with neuroscience, robotics, artificial intelligence, cognitive science, machine learning, and pattern recognition. This research topic is intended to provide a better understanding of the opportunities, challenges, and promising future directions for neurointelligence.
