scholarly journals Real-time classification of datasets with hardware embedded neuromorphic neural networks

2010 ◽  
Vol 11 (3) ◽  
pp. 348-363 ◽  
Author(s):  
L. Bako
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Real Time Classification

Intelligent Chair Sensor

2014 ◽  
Vol 3 (2) ◽  
pp. 65-80 ◽  
Author(s):  
Leonardo Martins ◽  
Rui Lucena ◽  
Rui Almeida ◽  
João Belo ◽  
Cláudia Quaresma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Mobile Application ◽  
Intelligent System ◽  
Conformation Change ◽  
The Neural Network ◽  
First Time ◽  
Best Parameters ◽  
Real Time Classification

In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data, obtaining an overall classification of around 80% for eleven standardized postures. Those neural networks were then exported to a mobile application to achieve a real-time classification of the standardized postures. Results showed a real-time classification of 93.4% for eight standardized postures, even for users that experimented for the first-time our intelligent chair. Using the same mobile application they devised and implemented two correction algorithms, acting due to conformation change of the bladders in the chair's seat when a poor seating posture is detected for certain periods of time.

Real-time classification of wooden boards

1991 ◽  
Author(s):  
Wolfgang Poelzleitner ◽  
Gert Schwingskakl
Keyword(s):  
Real Time ◽  
Real Time Classification

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.

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