scholarly journals Real-Time EMG Signal Classification via Recurrent Neural Networks

2021 ◽  
Author(s):  
Reza Bagherian Azhiri ◽  
Mohammad Esmaeili ◽  
Mehrdad Nourani
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Signal Classification ◽  
Emg Signal

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

scholarly journals Recurrent Neural Networks for real-time distributed collaborative prognostics

2018 ◽  
Author(s):  
Adria Salvador Palau ◽  
Kshitij Bakliwal ◽  
Maharshi Harshadbhai Dhada ◽  
Tim Pearce ◽  
Ajith Kumar Parlikad
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks

scholarly journals On the Adaptability of Recurrent Neural Networks for Real-Time Jazz Improvisation Accompaniment

2021 ◽  
Vol 3 ◽  
Author(s):  
Kosmas Kritsis ◽  
Theatina Kylafi ◽  
Maximos Kaliakatsos-Papakostas ◽  
Aggelos Pikrakis ◽  
Vassilis Katsouros
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Test Case ◽  
Jazz Improvisation ◽  
Artificial Agent ◽  
Separate Model ◽  
Subjective Evaluations ◽  
Refinement Process ◽  
Lead Sheet

Jazz improvisation on a given lead sheet with chords is an interesting scenario for studying the behaviour of artificial agents when they collaborate with humans. Specifically in jazz improvisation, the role of the accompanist is crucial for reflecting the harmonic and metric characteristics of a jazz standard, while identifying in real-time the intentions of the soloist and adapt the accompanying performance parameters accordingly. This paper presents a study on a basic implementation of an artificial jazz accompanist, which provides accompanying chord voicings to a human soloist that is conditioned by the soloing input and the harmonic and metric information provided in a lead sheet chart. The model of the artificial agent includes a separate model for predicting the intentions of the human soloist, towards providing proper accompaniment to the human performer in real-time. Simple implementations of Recurrent Neural Networks are employed both for modeling the predictions of the artificial agent and for modeling the expectations of human intention. A publicly available dataset is modified with a probabilistic refinement process for including all the necessary information for the task at hand and test-case compositions on two jazz standards show the ability of the system to comply with the harmonic constraints within the chart. Furthermore, the system is indicated to be able to provide varying output with different soloing conditions, while there is no significant sacrifice of “musicality” in generated music, as shown in subjective evaluations. Some important limitations that need to be addressed for obtaining more informative results on the potential of the examined approach are also discussed.

A VECTOR MATRIX REAL TIME RECURSIVE BACKPROPAGATION ALGORITHM FOR RECURRENT NEURAL NETWORKS THAT APPROXIMATE MULTI-VALUED PERIODIC FUNCTIONS

2009 ◽  
Vol 08 (04) ◽  
pp. 395-411 ◽  
Author(s):  
PETER STUBBERUD
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Recurrent Neural Networks ◽  
Feedforward Neural Networks ◽  
Universal Function ◽  
Multiple Delays ◽  
Feedback Path ◽  
Vector Matrix ◽  
Universal Approximators

Unlike feedforward neural networks (FFNN) which can act as universal function approximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTRBP) algorithm in a vector matrix form is developed for a two-layer globally recursive neural network that has multiple delays in its feedback path. This algorithm has been evaluated on two GRNNs that approximate both an analytic and nonanalytic periodic multi-valued function that a feedforward neural network is not capable of approximating.

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