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This work presents a neural network-based tactile object classification approach using a sensorized ...
Electromyography (EMG) is the technique of collecting electrical signals from the human body for fur...
The prediction of hand grasping and control of a robotic manipulator for hand activity training is o...
This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanica...
Abstract—Estimating human fingertip forces is required to understand force distribution in grasping ...
There is a growing interest in developing computational models of grasping action recognition. This ...
Abstract - Hierarchical and wider applications of robots, manipulators, and pick and place machines ...
In this paper, the feedforward neural network with Levenberg-Marquardt backpropagation training algo...
This study aimed to determine grip strength data for Turkish dentistry students and developed predic...
Optimal fingertip forces can always be computed through the well-known optimization algorithms. Howe...
To find out the feasibility of different neural networks in sEMG-based force estimation, in this pap...
Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous contr...
In this study a method has been introduced to map the features extracted from the recorded electromy...
Hand capture and animation is an important problem in Computer Animation because the hand is the mai...
Nölker C, Ritter H. Visual recognition of continuous hand postures. IEEE TRANSACTIONS ON NEURAL NETW...
This work presents a neural network-based tactile object classification approach using a sensorized ...
Electromyography (EMG) is the technique of collecting electrical signals from the human body for fur...
The prediction of hand grasping and control of a robotic manipulator for hand activity training is o...
This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanica...
Abstract—Estimating human fingertip forces is required to understand force distribution in grasping ...
There is a growing interest in developing computational models of grasping action recognition. This ...
Abstract - Hierarchical and wider applications of robots, manipulators, and pick and place machines ...
In this paper, the feedforward neural network with Levenberg-Marquardt backpropagation training algo...
This study aimed to determine grip strength data for Turkish dentistry students and developed predic...
Optimal fingertip forces can always be computed through the well-known optimization algorithms. Howe...
To find out the feasibility of different neural networks in sEMG-based force estimation, in this pap...
Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous contr...
In this study a method has been introduced to map the features extracted from the recorded electromy...
Hand capture and animation is an important problem in Computer Animation because the hand is the mai...
Nölker C, Ritter H. Visual recognition of continuous hand postures. IEEE TRANSACTIONS ON NEURAL NETW...
This work presents a neural network-based tactile object classification approach using a sensorized ...
Electromyography (EMG) is the technique of collecting electrical signals from the human body for fur...
The prediction of hand grasping and control of a robotic manipulator for hand activity training is o...