scholarly journals Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4782
Author(s):  
Huan-Chung Li ◽  
Telung Pan ◽  
Man-Hua Lee ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Speech Emotion Recognition ◽  
Single Observation ◽  
Artificial Neural

In recent years, many types of research have continued to improve the environment of human speech and emotion recognition. As facial emotion recognition has gradually matured through speech recognition, the result of this study provided more accurate recognition of complex human emotional performance, and speech emotion identification will be derived from human subjective interpretation into the use of computers to automatically interpret the speaker’s emotional expression. Focused on use in medical care, which can be used to understand the current feelings of physicians and patients during a visit, and improve the medical treatment through the relationship between illness and interaction. By transforming the voice data into a single observation segment per second, the first to the thirteenth dimensions of the frequency cestrum coefficients are used as speech emotion recognition eigenvalue vectors. Vectors for the eigenvalue vectors are maximum, minimum, average, median, and standard deviation, and there are 65 eigenvalues in total for the construction of an artificial neural network. The sentiment recognition system developed by the hospital is used as a comparison between the sentiment recognition results of the artificial neural network classification, and then use the foregoing results for a comprehensive analysis to understand the interaction between the doctor and the patient. Using this experimental module, the emotion recognition rate is 93.34%, and the accuracy rate of facial emotion recognition results can be 86.3%.

Deep Neural Network for Facial Emotion Recognition System

2021 ◽  
pp. 397-402
Author(s):  
Vimal Singh ◽  
Sonal Gandhi ◽  
Rajiv Kumar ◽  
Ramashankar Yadav ◽  
Shivani Joshi
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Deep Neural Network ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3170 ◽  
Author(s):  
Zhang ◽  
Yang ◽  
Qian ◽  
Zhang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Recognition System ◽  
Test Method ◽  
Training Dataset ◽  
Artificial Neural ◽  
Semg Signals

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

scholarly journals Recognition of Face Emotion using Convolutional Neural Network

2020 ◽  
Vol 9 (9) ◽  
pp. 364-376
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Recognition System ◽  
Video Stream ◽  
Machine Learning Algorithms ◽  
Facial Emotion Recognition ◽  
Facial Emotion

Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.

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