International Colloquium on Signal Processing and Its Applications 

About: International Colloquium on Signal Processing and Its Applications is an academic conference. The conference publishes majorly in the area(s): Feature extraction & Control theory. Over the lifetime, 1016 publications have been published by the conference receiving 8109 citations.

Physiological signals based human emotion Recognition: a review

Abstract: Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.

Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO

Abstract: The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI) This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment The system utilizes external cameras, besides the robot's own visual system Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session

Using Hidden Markov Models for accelerometer-based biometric gait recognition

Abstract: Biometric gait recognition based on accelerometer data is still a new field of research. It has the merit of offering an unobtrusive and hence user-friendly method for authentication on mobile phones. Most publications in this area are based on extracting cycles (two steps) from the gait data which are later used as features in the authentication process. In this paper the application of Hidden Markov Models is proposed instead. These have already been successfully implemented in speaker recognition systems. The advantage is that no error-prone cycle extraction has to be performed, but the accelerometer data can be directly used to construct the model and thus form the basis for successful recognition. Testing this method with accelerometer data of 48 subjects recorded using a commercial of the shelve mobile phone a false non match rate (FNMR) of 10.42% at a false match rate (FMR) of 10.29% was obtained. This is half of the error rate obtained when applying an advanced cycle extraction method to the same data set in previous work.

Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT)

Abstract: Human emotion recognition plays a vital role in psychology, psycho-physiology and human machine interface (HMI) design. Electroencephalogram (EEG) reflects the internal emotional state changes of the subject compared to other conventional methods (face recognition, gestures, speech, etc). In this work, EEG signals are collected using 62 channels from 20 subjects in the age group of 21~39 years for determining discrete emotions. Audio-visual stimuli (video clips) is used for inducing five different emotions (happy, surprise, fear, disgust, neutral). EEG signals are preprocessed through Butterworth 4th order filter with a cut off frequency of 0.5 Hz-60 Hz and smoothened using Surface Laplacian filter. EEG signals are framed into a short time duration of 5s and two statistical features (spectral centroid and spectral entropy) in four frequency bands namely alpha (8 Hz-16 Hz), beta (16 Hz-32 Hz), gamma (32 Hz-60 Hz) and alpha to gamma (8 Hz-60 Hz) are extracted using Fast Fourier Transform (FFT). These features are mapped into the corresponding emotions using two simple classifiers such as K Nearest Neighbor(KNN) and Probabilistic Neural Network (PNN). In this work, KNN outperforms PNN by offering the maximum mean classification accuracy of 91.33 % on beta band. This experimental results indicates the short time duration of EEG signals is highly essential for detecting the emotional state changes of the subjects.

Recognition and detection of tea leaf's diseases using support vector machine

Abstract: Tea is a popular beverage all around the world, and in Bangladesh the cultivation of tea plays a vital role. Many diseases affect the proper growth of tea leaves leading to its reduction, thus hindering of the production of tea. However, if the disease is identified at an early age it would solve all the above mentioned problems through the application of appropriate treatment, or through the pruning of the diseased leaves to prevent further spread of the disease. To solve this problem image processing is the best option to detect and diagnose the disease. The main goal of this research is to develop an image processing system that can identify and classify the two most widespread tea leaf diseases in Bangladesh, namely brown blight disease and the algal leaf disease, from a healthy leaf. Disease identification is the first step; there are many methods that have been used for identifying the leaf disease. In this paper, Support Vector Machine classifier (SVM) is used to recognize the diseases. Eleven features are analyzed during the classification. These features are then used to find the most suitable match for the disease (or normality) every time an image is uploaded into the SVM database. When a new picture is uploaded into the system the most suitable match is found and the disease is recognized. The approach is novel since the number of features compared by the SVM classifier is reduced by three features compared to previous researches, without adversely sacrificing the success rate of the classifier, which retains an accuracy of more than 90%. This also speeds up the identification process, with each leaf image taking 300ms less processing time compared to previous research using SVM, thus ensuring a greater number of leaves can be processed in a given time frame. The proposed solution increases in efficiency of the detection, identification, and classification process will enable the tea industry in Bangladesh to become more competitive globally, by reducing the losses suffered due to diseases of the leaf, and thus increasing the overall tea production rate.