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The rehabilitation exoskeleton robot is more and more used in the assisting stroke patients in implementing rehabilitation training. In this paper, a novel exoskeleton hand robot which was driven by cable has been proposed to aim at helping varieties of paralyses patients recover motor function. This exoskeleton hand rehabilitation robot system mainly consists of exoskeleton hand robot, EEG system,...
The recovery of motor function after stroke is widely considered to result from brain plasticity. However, what kind of training exercise can better provoke brain plastic processes is still unclear. Studying regional brain activation during a specific training exercise may provide value information that can help design more effective therapeutic approaches. In this paper, we monitored brain activation...
Detecting and interpreting contacts is a crucial aspect of physical Human-Robot Interaction. In order to discriminate between intended and unintended contact types, we derive a set of linear and non-linear features based on physical contact model insights and from observing real impact data that may even rely on proprioceptive sensation only. We implement a classification system with a standard non-linear...
Inspired by achievements in rehabilitation, motor learning, and neuroscience, therapeutic robots are aiming to provoke neuromotor plasticity and improve recovery after stroke and mobility impairments. Human sensorimotor system is specialized with position and velocity sensory fibers and exhibits variant muscle impedance in accordance with the ongoing task. The virtually interfaced robotic ankle and...
A learning system is presented which uses feedforward control to improve the accuracy of standard position controlled robots. The method is executed on joint level since in this case there are less couplings than in the cartesian space. On the other side the main goal is to reduce the maximal deviation from a given cartesian path. This requires extended algorithms which are derived and examined using...
Our aim is to better understand the action selection process of intelligent systems by looking at their ability of internal prediction. In robotic systems, one problem is to generate meaningful robot behaviour with a very small and simple set of trained motions. An additional problem is to compensate for incomplete sensory data while generating behaviour. We propose a new predictive action selector...
The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character's strokes. This approach derives the font information from human gestures by...
Virtual Reality (VR) presents a promising future in the field of rehabilitation due to its advantages brought to the training process as is indicated in many articles and researches. In this paper, we describe a novel method in developing a virtual training environment for a 5 degrees of freedom (DOF) upper limb rehabilitation robot which has been designed to help provide assistance for patients who...
The paper presents a new method to realize mobile robot arm grasping in indoor laboratory environments. This method adopts a blind strategy, which does not need the robot arms be mounted any kind sensors and avoid calculating the complex kinematic equations of the arms. The method includes: (a) two robot on-board ultrasonic sensors in base are utilized to measure the distances between the robot base...
The restoration of walking capability is very important after central nervous system (CNS) injury. Rehabilitation training robot can replace therapists to help patients do walking training by accurately simulating the human lower limb movement, and achieve the purpose of rehabilitation ultimately. In order to further improve the rehabilitation robots' intelligent and rehabilitation training effect...
Rehabilitation robotic devices have been actively explored for training patients with impaired neural functions or assisting those with weak limbs due to aging or diseases. In recent years, the authors have proposed light-weight exoskeleton designs for the upper arm, in which rigid links of the exoskeleton are replaced by lightweight cuffs attached to the moving limb segments of the human arm. Cables,...
Emerging technologies such as rehabilitation robots (RehaBot) for retraining upper and lower limb functions have shown to carry tremendous potential to improve rehabilitation outcomes. Hstar Technologies is developing a revolutionary rehabilitation robot system enhancing healthcare quality for patients with neurological and muscular injuries or functional impairments. The design of RehaBot is a safe...
In this research, the back-propagation neural network system and the ACCESS database management are implemented for on-line learning of the ankle joint of the biped robot walking forward. The system acquires the inclining angles of the center of gravity by the inclination sensor and the joint angles by the AI servo motors when the manual teaching is applied for the on-line learning templates of the...
This paper presents the kinematic design of a single degree-of-freedom exoskeleton mechanism: a planar eight-bar mechanism for finger curling. The mechanism is part of a finger-thumb robotic device for hand therapy that will allow users to practice key pinch grip and finger-thumb opposition, allowing discrete control inputs for playing notes on a musical gaming interface. This approach uses the mechanism...
A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a...
The investigation and characterization of sensori-motor learning and execution represents a key objective for the design of optimal rehabilitation therapies following stroke. By supplying new tools to investigate sensorimotor learning and objectively assess recovery, robot assisted techniques have opened new lines of research in neurorehabilitation aiming to complement current clinical strategies...
Compressed Sparse Code Hierarchical Self-Organizing Map (CoSCo-HSOM) is an extension of ideas existent in the gesture classification and recognition research area. Building on Hierarchical Self-Organizing systems and cognitive models introduced by neuropsychologists, we present the CoSCo-HSOM algorithm introducing novel features to the previously published sparse encoding HSOM model. During the training...
This paper addresses the problem of hand-eye coordination and, more specifically, tool-eye recalibration of humanoid robots. Inspired by results from neuroscience, a novel method to learn the forward kinematics model as part of the body schema of humanoid robots is presented. By making extensive use of techniques borrowed from the field of computer-aided geometry, the proposed kinematic Bezier maps...
The current paper shows a neuro-Robotics experiment on developmental learning of goal-directed actions. The robot was trained to predict visuo-proprioceptive flow of achieving a set of goal-directed behaviors through iterative tutor training processes. The learning was conducted by employing a dynamic neural network model which is characterized by their multiple time-scales dynamics. The experimental...
In recent years, many researchers have studied on the rehabilitation robotics to assist medical staff or patients. Some kinds of haptic devices have been developed and evaluated its efficiency with clinical tests, for example, upper limb training for patients with spasticity after stroke. Almost all the devices for upper limb rehabilitation have only 2-DOF for its active motion except for wrists....
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