Predicting and improving the recognition of emotions

The technological world is moving towards more effective and friendly human computer interaction. A key factor of these emerging requirements is the ability of future systems to recognise human emotions, since emotional information is an important part of human-human communication and is therefore expected to be essential in natural and intelligent human-computer interaction. Extensive research has been done on emotion recognition using facial expressions, but all of these methods rely mainly on the results of some classifier based on the apparent expressions. However, the results of classifier may be badly affected by the noise including occlusions, inappropriate lighting conditions, sudden movement of head and body, talking, and other possible problems. In this paper, we propose a system using exponential moving averages and Markov chain to improve the classifier results and somewhat predict the future emotions by taking into account the current as well as previous emotions

Emotive computing may have a role in telecare

This brief paper sets out arguments for the introduction of new technologies into telecare and lifestyle monitoring that can detect and monitor the emotive state of patients. The significantly increased use of computers by older people will enable the elements of emotive computing to be integrated with features such as keyboards and webcams, to provide additional information on emotional state. When this is combined with other data, there will be significant opportunities for system enhancement and the identification of changes in user status, and hence of need. The ubiquity of home computing makes the keyboard a very attractive, economic and non-intrusive means of data collection and analysis

Emotion recognition using electroencephalogram signal

Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then, two ranges of frequency bands were extracted from the signals which are alpha and beta. Finally, these samples will be classified using MLP Neural Network. Classification accuracy up to 91% is achieved and the average percentage of accuracy for calm, fear, happy and sad are 83.5%, 87.3%, 85.83% and 87.6% respectively. Thus, a proof of concept, this study has been capable of proposing a system of recognizing four states of emotion which are happy, sad, fear and calm by using EEG signal

Sadness Detection for Future Smart Homes

This thesis focuses on sadness detection and recognition using deep learning and image processing in python. It analyzes accurate and efficient ways to collect a large set of “moments” from YouTube videos to build large-scale databases for “moments” that show the emotion of sadness. For the overall model architecture, a sequential neural network model is built with three fully connected convolutional layers and rectified linear units as our activation function. Initially, we obtain a nearly zero false positive rate and around ten percent false negative rate on this trained model. To further improve the accuracy and efficiency, the Haar Cascade classifier is used to crop only frontal face images and OpenPose is used to locate facial key points to precisely detect and analyze the facial expression. Besides, we crawl the YouTube network to acquire the video information and used natural language processing to filter the videos that are more likely to contain the emotion sadness. By incorporating the deep learning model with the above algorithms, “moments” that contain the emotion of sadness are extracted from YouTube videos and output as a JSON file, which can be viewed via the iLab AI-Human Video Database

A physiological signal database of children with different special needs for stress recognition

This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children's videos, and physiological data is labeled "Stress/No Stress" and "Reaction/No Reaction", according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.Scientific and Technological Research Council of Turkey Technology and Innovation Funding Programmes Directorat

Emotion Recognition using Fuzzy Clustering Analysis

This research project investigates using fuzzy clustering algorithms for emotion recognition. Emotion recognition has gained significant attention in recent years in applications such as artificial intelligence, human-computer interaction, speech and voice recognition. The ability of a computer or machine to understand human emotion and respond to users in a more human way can lead to significant advances in conversational speech recognition systems, improved quality of life in persons with speech disorders, such as Parkinson’s disease and even in voice response systems, such as Google Voice or Apple’s Siri. Experimental results in this area can inform discovery and innovation of machine intelligence and actionable response algorithms that use physiological methods for characterizing speech. Human emotion is a complex signal that is difficult to characterize analytically. One proposed method for characterizing emotion is to use fuzzy clustering techniques to partition the data into classifications of emotions based on feature similarities. Fuzzy clustering provides a method for organizing data into groups either in unsupervised fashion or based on the selected feature and classifying each group as a different emotion. In this work, an emotional prosody speech dataset is used as input to a fuzzy clustering toolbox to explore underlying structures in the dataset and perform data reduction for optimal feature extraction. The emotion dataset includes fifteen different categories of emotions: happy, elation, sadness, despair, boredom, interest, shame, pride, contempt, disgust, panic, anxiety, hot anger, cold anger, and no emotion. The goal of this research project is to identify a fuzzy clustering technique that will partition the dataset into different categories of emotions. Furthermore, the expected results should illustrate that similar emotions (e.g. sadness and despair) may exhibit similar patterns in classification, and thus may not by recognized as two separate categories by the fuzzy clustering analysis

10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017

Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017. Procedia Computer Science, 108, 1113-1114. https://doi.org/10.1016/j.procs.2017.05.279We present the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017, held in Zurich, 12 - 14 June 2017.publishersversionpublishe