Multifunctional Electrical Prosthetic Hand -Development of Tendon-driven Mechanism and Controller

2002 ◽  
Vol 14 (6) ◽  
pp. 557-564 ◽  
Author(s):  
Wenwei Yu ◽  
◽  
Daisuke Nishikawa ◽  
Yasuhiro Ishikawa ◽  
Hiroshi Yokoi ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Joint Torque ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
Nonlinear Properties ◽  
The Stability

The purpose of this research was to develop a tendondriven electrical prosthetic hand, which is characterized by its mechanical torque-velocity converter and a mechanism that can assist proximal joint torque by distal actuators. To cope with time-delay and nonlinear properties of the prosthetic hand, a controller based on a Jordan network, recurrent neural network models, is proposed. The results of experiments on the stability of the controller are confirmed when tracking static wire tensions. Finally, the next prototype of prosthetic hand based on these methods is introduced.

Recurrent Neural Network Models for Myoelectricbased Control of a Prosthetic Hand

2018 ◽  
Author(s):  
Teodor-Adrian Teban ◽  
Radu-Emil Precup ◽  
Elena-Cristina Lunca ◽  
Adriana Albu ◽  
Claudia-Adina Bojan-Dragos ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Prosthetic Hand ◽  
Neural Network Models

Convergence Theory for Unified Discrete-Time RNNs with Quasi-Symmetric Connection

2014 ◽  
Vol 538 ◽  
pp. 167-170
Author(s):  
Hui Zhong Mao ◽  
Chen Qiao ◽  
Wen Feng Jing ◽  
Xi Chen ◽  
Jin Qin Mao
Keyword(s):  
Neural Network ◽  
Global Convergence ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Symmetric Matrix ◽  
Network Models ◽  
Convergence Theory ◽  
Connection Matrix ◽  
Neural Network Models ◽  
Symmetric Connection

This paper presents the global convergence theory of the discrete-time uniform pseudo projection anti-monotone network with the quasi–symmetric matrix, which removes the connection matrix constraints. The theory widens the range of applications of the discrete–time uniform pseudo projection anti–monotone network and is valid for many kinds of discrete recurrent neural network models.

scholarly journals PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Soroosh Shahtalebi ◽  
Seyed Farokh Atashzar ◽  
Olivia Samotus ◽  
Rajni V. Patel ◽  
Mandar S. Jog ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Hand Tremor ◽  
Deep Mining

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

2019 ◽  
Vol 14 (2) ◽  
pp. 158-164 ◽  
Author(s):  
G. Emayavaramban ◽  
A. Amudha ◽  
T. Rajendran ◽  
M. Sivaramkumar ◽  
K. Balachandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Network Models ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
The Neural Network ◽  
Semg Signals

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

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