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Generalized Net Model of an Active Elbow Orthosis Prototype
Abstract
This paper presents a novel approach for describing the functioning of an active elbow orthosis with the use of generalized nets modeling. The so proposed model will permit the development of user-oriented control of sEMG- powered elbow orthosis. We propose an abstract model based on the “on-off” myoelectric control, appropriate for maximum two degrees of freedom in the elbow joint.
Generalized Nets (GNs) are a powerful tool for discrete event simulation and parallel processes flow representation. The apparatus of GNs is equally well suited for modelling simple systems, as well as large, complex systems. The major strength of a discrete event simulation is its ability to model random events and to predict the effects of complex interactions between these events. GN-models could be used as a quick method for analysing and solving complex problems. This article presents a brief overview of the evolution of the GNs theory and its various fields of application. The results discussed here are based on years of research by scientists of the Bulgarian Academy of Sciences.
Proximal humeral fractures are common injuries and they occur primarily in older patients. They represent 5.7% of diagnosed fractures and are the third most common fracture pattern seen in elderly. Fractures of the proximal humerus usually occur after a high or low energy fall. Due to the variety of factors, which influence the classification, and the diagnosis of these fractures, early detection is the key factor for an appropriate treatment. Accordingly, in this study we present a successful example of Generalized Nets application in traumatology and propose a novel approach to timely detection and diagnosing of proximal humeral fractures.
Modular Robotic Systems (MRSs) are entities, composed of units, that can be reconfigured independently for group (swarm), solving different tasks. These robots have many potential applications in hazardous and unknown environments like exploration of other planets in the universe. Such systems have to be able to deal with dynamic changes in the environment that potentially lead to failures and goal changes. Groups of robots will need to plan, execute, and then re-plan in real-time, while simultaneously being robust enough to avoid robot damage and complete mission failure. Generalized Nets (GNs), on the other hand, are ascertained at the present paper because they have proved to be very useful and convenient tool for description, modelling and training of different elements from the Artificial Intelligence area, including robotics. The paper will discuss some possibilities and results for Generalized Nets representation, modelling and control of mobile modular robotic systems.
Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human Central Nervous System of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of 10 healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement in different dynamic conditions (i.e. no alterations of movement accuracy are observed).
In this article the design of a new upper limb rehabilitation system will be presented. A lightweight, modular, and portable system is achieved by the combination of electromyographic (EMG) control, functional electrical stimulation (FES), and the use of miniaturized flexible fluidic actuators (FFA) integrated in an elbow orthosis. First, the state of the art of upper limb rehabilitation devices will be discussed and requirements extracted. Then, the design concept of the new prototype upper limb training system will be presented. Subsequently, a miniaturized fluidically driven actuation system, including its mechatronical components, will be highlighted. Finally, an overview of the performance and function will be given.
Short remarks on Generalized net theory are given. Some possible applications of the generalized net apparatus as means for modelling of data-mining processes are discussed.
The paper presents the electromyogram (EMG)-based neural network control of an upper-limb power-assist exoskeleton robot, which is proposed to control the robot in accordance with the user's motion intention. The upper limb rehabilitation exoskeleton is with high precision for co-manipulation tasks of human and robot because of its backdrivability, precise positioning capabilities, and zero backlash due to its harmonic drive transmission (HDT). The novelty of this work is the development of an adaptive neural network modeling and control approach to handle the unknown parameters of the harmonic drive transmission in the robot to facilitate motion control. We have conducted the experiments on human subject to identify the various parameters of the harmonic drive system combining sEMG information signals.
Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.
Generalized net model of surface EMG data processing for control of active elbow orthosis device
- S Ribagin
- T Pencheva
- T Shannon
Generalized net model for user-oriented control of an active elbow orthosis device
- S Ribagin
- K Atanassov
- K Atanassov
The orthotic prescription
- T R Lunsford
- J M Wallace
- TR Lunsford
Generalized Nets in Medicine
- A Shannon
- J Sorsich
- K Atanassov
Generalized Nets and Genetic Algorithms
- O Roeva
- T Pencheva
- A Shannon
- K Atanassov
Upper-limb orthotics
- H R Lehneis
- HR Lehneis