scholarly journals Real-time Continuous Hand Motion Myoelectric Decoding by Automated Data Labeling

2019 ◽  
Author(s):  
Xuhui Hu ◽  
Hong Zeng ◽  
Dapeng Chen ◽  
Jiahang Zhu ◽  
Aiguo Song
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Coefficient Of Determination ◽  
Residual Limb ◽  
Training Set ◽  
Real Motion ◽  
Motion Measurement ◽  
Hand Motion ◽  
Fitts Law ◽  
Unsupervised Neural Network

AbstractIn this paper an automated data labeling (ADL) neural network was proposed to streamline dataset collecting for real-time predicting the continuous motion of hand and wrist, these gestures are only decoded from a surface electromyography (sEMG) array of eight channels. Unlike collecting both the bio-signals and hand motion signals as samples and labels in supervised learning, this algorithm only collects the unlabeled sEMG into an unsupervised neural network, in which the hand motion labels are auto-generated. The coefficient of determination (r2) for three DOFs, i.e. wrist flex/extension, wrist pro/supination, hand open/close, was 0.86, and 0.87 respectively. The comparison between real motion labels and auto-generated labels shows that the latter has earlier response than former. The results of Fitts’ law test indicate that ADL has capability of controlling multi-DOFs simultaneously even though the training set only contains sEMG data from single DOF gesture. Moreover, no more hand motion measurement needed which greatly helps upper-limb amputee imagine the gesture of residual limb to control a dexterous prosthesis.

scholarly journals Neural network adaptive real-time optimizing control of industrial processes

2017 ◽  
Vol 19 ◽  
pp. 129
Author(s):  
Normah Abdullah ◽  
Muhammad Anas Mohd Razali ◽  
Mohammed Hamood Othman Ahmed ◽  
Mohd Zaki Nuawi ◽  
Mohd Marzuki Mustafa ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Process Model ◽  
Chemical Engineering ◽  
Coefficient Of Determination ◽  
Engineering Research ◽  
In Series ◽  
Artificial Neural Network Ann ◽  
Optimizing Control

<p>Real-time optimization (RTO) has attracted considerable interest among researchers and industries for being able to optimise the plant economics such as product efficiency, product quality and process safety in the wake of increasing global competitions. The success of RTO depends much on the quality of model being used in the optimisation. The present study was carried out to explore the use of artificial neural network (ANN) to improve the quality of the model being used in the modified two step (MTS) technique. The MTS is a real-time optimising control algorithm of the modifier adaptation scheme which is used to determine the optimum steady-state control set-points. The proposed new version of MTS technique will be using process model based on ANN. A laboratory scale process of a two continuous stirred tank heat exchanger in series (2CSTHEs) is used as a case study. The multilayer feed forward ANN architecture 4-10-6 with linear function was used to model the 2CSTHEs and then integrates into the MTS technique, the resulted algorithm will be known as Iterative Neural Network Modified Two Step (INNMTS) technique. Simulation studies were conducted to test the performance of the INNMTS technique on the 2CSTHEs process. The results show that the overall value for the coefficient of determination(R<sup>2</sup>)is equal to one, which indicates adequacy of the model proposed for the prediction of the behavior of 2CSTHEs system. When NN model of 2CSTHEs is applied to the INNMTS technique, the model-plant mismatch is greatly reduced to almost zero, which indicates by significant reduction in the number of iterations to 5which requires by INNMTS compared to 16 iterations by the MTS technique to converge to optimal real solution.</p><p>Chemical Engineering Research Bulletin 19(2017) 129-138</p>

scholarly journals The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8365
Author(s):  
Xianfu Zhang ◽  
Yuping Hu ◽  
Ruimin Luo ◽  
Chao Li ◽  
Zhichuan Tang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Lower Limb ◽  
Gait Recognition ◽  
Training Set ◽  
Lower Limb Exoskeleton ◽  
The Impact ◽  
Semg Signals

Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.

scholarly journals Surface Electromyography Hand Motion Classification Using Time Domain Features and Artificial Neural Network for Real Time Application

2014 ◽  
Vol 6 (8) ◽  
pp. 917-920 ◽  
Author(s):  
Ahmad Akmal Ahmad Nadzri ◽  
Mohd Hanif Mohamad Zaini ◽  
Siti Anom Ahmad ◽  
Mohd Hamiruce Marhaban ◽  
Haslina Jaafar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Time Domain ◽  
Surface Electromyography ◽  
Motion Classification ◽  
Hand Motion ◽  
Artificial Neural ◽  
Real Time Application

A real-time, unsupervised neural network for the low-level control of a mobile robot in a nonstationary environment

1995 ◽  
Vol 8 (1) ◽  
pp. 103-123 ◽  
Author(s):  
Eduardo Zalama ◽  
Paolo Gaudiano ◽  
Juan López Coronado
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Real Time ◽  
Level Control ◽  
Low Level ◽  
Unsupervised Neural Network

Real Time Object Identification Using Neural Network with Caffe Model

2019 ◽  
Vol 7 (5) ◽  
pp. 175-182
Author(s):  
Anjali Nema ◽  
Anshul Khurana
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Object Identification
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