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The interaction with robotic devices by means of physiological human signals has become of great interest in the last years because of the capability of catching human intention of movement and translate it in a coherent action performed by a robotic platform. Due to the complexity of EMG signals, several studies have been carried out about models built on a single subject (subject-specific). However,...
This paper proposes two robot-assisted exercise training methods for knee rehabilitation based on a practical EMG-driven model, aiming to beneficially exploit the patient's ability through neurorehabilitation process. The EMG-driven model is a simplified representation of the musculoskeletal system, with acceptable accuracies to predict the muscle forces and active torque of knee joint. Thus the patient's...
In recent years, actuation technology have been increasingly developed new fields and utilized widely in applications differing from automation and industry , but also robotic rehabilitation, haptics and wearable exoskeleton devices where safety, limitation of peak forces and gentle interaction are extremely important. To date, several examples of robotic applications have been designed to address...
In open-ended domains, autonomous robots must have the ability to continuously process visual information, and execute learning and recognition in a concurrent and interleaved fashion. Because the set of categories to be learned is not predefined, it is not feasible to assume that one can pre-program all object categories required by service robots. Topic modelling approaches usually construct the...
Upper limb rehabilitation robot is used to assist in completing rehabilitation training for patients with upper limb disorder. It is inevitable that the control system is probably disrupted by the patients in the process of rehabilitation training so that the movement of the rehabilitation robot is not smooth. This problem is not conductive to rehabilitation. In view of the contour tracking method...
Brain-machine interface (BMI) systems collect and classify electroencephalogram (EEG) data to predict the desired command of the user. The P300 EEG signal is passively produced when a user observes or hears a desired stimulus. The P300 can be used with a visual display to allow a BMI user to select commands from an array of selections. The visual stimuli are often repeated and averaged to increase...
This paper proposes a new method of muscle force estimation based on the relationship between the muscle space and the task space using a general lower extremity model in the sagittal plane. In addition, we report a verification experiment using several active motion patterns with a training robot for the lower extremities. The results show that the muscle forces estimated by the proposed method exhibit...
As part of ongoing work to create training for Mission Critical Operators, a new training system was developed offering multiple degrees of freedom (DOF), which was a desirable addition as part of a progression of training systems of increasing complexity. With the addition of multiple DOF, the user may be exposed to a more complex control environment presenting challenging tasks related to full range...
There are many alternative human-computer interfaces that are in active development in current time. For example, a user already can send commands to robotic device via brain-computer interface. Unfortunately, this control channel isn't reliable enough to handle critical devices, but it can still be useful to clarify commands from other control channels. The robotic device operator can be influenced...
This paper focuses on the real time control of autonomous robot through the detection of multiple types of motor imagery. The EEG data contains when subject performs imagery motor activity. The motor imagery activity includes movements of left hand, right hand, left leg and right leg. For feature extraction, the cross-correlation method is used to calculate six statistical features (mean, median,...
Stroke, traumatic brain injury (TBI), and spinal cord injury (SCI) are most important reasons that cause nervous system damage, and thus lead to physical disabilities. Because of long process of nervous system recovery, the patients would suffer permanent disability without effective treatment and rehabilitation. For traditional exercise therapy, most hospitals still use simple cycling devices for...
In this paper we discuss the concept of co-adaptation between a human operator and a machine interface and we summarize its application with emphasis on two different domains, teleoperation and assistive technology. The analysis of the literature reveals that only in a few cases the possibility of a temporal evolution of the co-adaptation parameters has been considered. In particular, it has been...
In this paper, a novel robust adaptive control scheme is proposed for a lower limb rehabilitation robot designed by our laboratory. The proposed control strategy is based on the radial basis function (RBF) neural networks and the parameters of the system dynamics are unknown. The weights of the RBF neural networks are updated by an adaptive law according to the Lyapunov stability analysis. The robustness...
Patients with upper limb disabilities caused by neurologic or orthopedic disorder required rehabilitation for the motor function recovery. In this research, based on a novel physical therapy, "bilateral arm training", the bilateral control interface connecting an IMU system and the exoskeleton robot NTUH-II is developed for patients to conduct passive or active-assistive rehabilitation....
Mobility impairment is one of the biggest challenges faced by elderly people in today's society. The inability to move about freely poses severe restrictions on their independence and general quality of life. This work is dedicated to developing intelligent robotic platforms that assist users to move without requiring a human attendant. This work was done in the context of an EU project involved in...
We present a study on the tradeoffs between three design parameters for robotic task systems that function in partially unknown and unstructured environments, and under timing constraints. The design space of these robotic tasks must incorporate at least three dimensions: (1) the amount of training effort to teach the robot to perform the task, (2) the time available to complete the task from the...
Noise robust speech recognition is crucial for effective human-machine interaction in real-world environments. Sound source separation (SSS) is one of the most widely used approaches for addressing noise robust speech recognition by extracting a target speaker's speech signal while suppressing simultaneous unintended signals. However, conventional SSS algorithms, such as independent component analysis...
Derived from ecological psychology, the term ‘affordance’ refers to the functional classification of objects. It simply means the set of actions a subject (i.e. humans and anthropomorphic agents) can possibly perform with an object. There are several paradigms in the researches regarding the approaches of affordance detection. These approaches include considering other contexts like the subject, ambient...
Deictic gestures - pointing at things in human-human collaborative tasks - constitute a pervasive, non-verbal way of communication, used e.g. to direct attention towards objects of interest. In a human-robot interactive scenario, in order to delegate tasks from a human to a robot, one of the key requirements is to recognize and estimate the pose of the pointing gesture. Standard approaches rely on...
Human action recognition plays a vital role in the field of human-robot interaction and is widely researched for its potential applications. In this paper we propose a human action recognition framework for human robot interaction in industrial applications. First, a set of key descriptors are learned from a collection of weak spatio-temporal skeletal joint descriptors using random forests, which...
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