Khairunizam Wan

Physiological signals based human emotion Recognition: a review

Recent research in the field of Human Computer Interaction aims at recognizing the users 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.

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

BackgroundIdentifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.MethodsEmotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.ResultsAnalysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.ConclusionsThe results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

Electrocardiogram-based emotion recognition system using empirical mode decomposition and discrete Fourier transform

Emotion recognition using physiological signals has gained momentum in the field of human computer-interaction. This work focuses on developing a user-independent emotion recognition system that would classify five emotions happiness, sadness, fear, surprise and disgust and neutral state. The various stages such as design of emotion elicitation protocol, data acquisition, pre-processing, feature extraction and classification are discussed. Emotional data were obtained from 30 undergraduate students by using emotional video clips. Power and entropy features were obtained in three ways - by decomposing and reconstructing the signal using empirical mode decomposition, by using a Hilbert-Huang transform and by applying a discrete Fourier transform to the intrinsic mode functions IMFs. Statistical analysis using analysis of variance indicates significant differences among the six emotional states p<0.001. Classification results indicate that applying the discrete Fourier transform instead of the Hilbert transform to the IMFs provides comparatively better accuracy for all the six classes with an overall accuracy of 52%. Although the accuracy is less, it reveals the possibility of developing a system that could identify the six emotional states in a user-independent manner using electrocardiogram signals. The accuracy of the system can be improved by investigating the power and entropy of the individual IMFs.

Emotion recognition from facial EMG signals using higher order statistics and principal component analysis

Higher order statistics (HOS) is an efficient feature extraction method used in diverse applications such as bio signal processing, seismic data processing, image processing, sonar, and radar. In this work, we have investigated the application of HOS to derive a set of features from facial electromyography (fEMG) signals for classifying six emotional states (happy, sad, afraid, surprised, disgusted, and neutral). fEMG signals were collected from different types of subjects in a controlled environment using audio-visual (film clips/ video clips) stimuli. Acquired fEMG signals were preprocessed using moving average filter and a set of statistical features were extracted from fEMG signals. Extracted features were mapped into corresponding emotions using k-nearest neighbor classifier. Principal component analysis was utilized to analyze the efficacy of HOS features over conventional statistical features on retaining the emotional information retrieval from fEMG signals. The results of this work indicate an improved mean emotion recognition rate of 69.5% from this proposed methodology to recognize six emotional states.

Emotion recognition from electrocardiogram signals using Hilbert Huang Transform

Equipping robots and computers with emotional intelligence is becoming important in Human-Computer Interaction (HCI). Bio-signal based methods are found to be reliable and accurate than conventional methods as they directly manifest the underlying activity of the Autonomous Nervous System (ANS). This paper focuses on recognizing six emotional states (happiness, sadness, fear, surprise, disgust and neutral) from Electrocardiogram (ECG) signals that were obtained from multiple subjects. The emotional data was collected by inducing emotions internally in the subject using audio visual clips. The normalized QRS derivative signal was obtained from captured emotional ECG data by means of a non-linear transform. Hilbert Huang Transform (HHT) based analysis was done to obtain the emotional features in low, high and total (low and high together) the frequency ranges. The classification results indicate that low frequency Intrinsic Mode Functions (IMF) contain more emotional information compared to the other frequency ranges. The performance of the system can be improved further by analyzing the information in the low frequency range.

NMPC-PID based control structure design for avoiding uncertainties in attitude and altitude tracking control of quad-rotor (UAV)

The extensive consideration in this research article is to utilize the advantages of two most popular control techniques which are Non-Linear Model Predictive Control (NMPC) and Proportional Integral and Derivative (PID) controller for better stabilizing of quad-rotor VAV under different noises and disturbance conditions. The idea is to satisfy the environmental and safety considerations and for that the study of noises and disturbance condition in VAV flight upon the performances of NMPC and PID respectively is being evaluated. Finally a new control method is developed by combing two techniques which can be able to handle different sort of uncertainties i.e. noises and external disturbances in quad-rotor type VAV systems. The simulation result proves that the proposed control structure technique works very well in altitude and attitude stabilization of quad-rotor under different perturbed and unperturbed conditions.

Analysis of Finger Movement for Robotic Hand (MAPRoh-1) by Using Motion Capture and Flexible Bend Sensor

Since the beginning of twentieth century, human–computer interaction (HCI) and humanoid robot has been the trend of advance countries to show their achievement in the technology. Thus, it is rituals for others develop countries to tail their footstep. By means of self-construct robotic hand based on human hand behaviors, an experiment was conducted to investigate the characteristic of robotic finger movements. The purpose of this paper is to analyze the correlation between angle produced by motion capture system (MOCAP) and voltage produced by the flexible bend sensor attached to the robotic hand. At the end of the project, the relationship regarding both angle and voltage will be clarified by using regression method and the preliminary result indicates that voltage and angle variation is possibly linear to each other based on correlation of coefficient outcome.

Upper Extremity Vein Graft Monitoring Device after Surgery Procedure: A Preliminary Study

In most cases, surgical vein bypass or interposition vein grafting was used in both primary management of crush-avulsion amputations and on intervention for rehabilitating the patency of occluded arteries via microvascular surgery. However, surgical revascularization has significant shortcomings, principal among which is the high rate of accelerated thrombosis that develops in arterialised vein graft which renders the vein graft susceptible to acute occlusion and eventually give rise to graft failure. Evaluaion and detection of vein graft failure is essential as that will be the starting point for the clinician to make the diagnosis and safeguard patency of implanted vein graft which would otherwise fail. Unfortunately, most of the available diagnostic and monitoring tools available in the market are expensive, hence not all the hospital, private clinic and others medical centers that fully-equipped with these type of equipments. The objective of this study is to design and develop a low-cost and non-invasive vein graft monitoring prototype that able to provide high accuracy in predicting the vein graft patency and meanwhile providing the short-term monitoring on vein graft right after surgery procedure. Impedance plethysmography (IPG) was employed to measure pulsatile changes in longitudinal impedace to quantify arterial blood flow and pulsatile blood volume. Tetra-polar electrode measurement system was implemented by introduce a constant 1-mA AC current (I) at frequency of 100 kHz in the two outer electrodes. The voltage (V) is measured between the two inner electrodes, and the resulting impedance (Z) is calculated using Ohm’s Law. Arterial blood flow and pulsatile blood volume can then be estimated using impedance related volume conduction equation. By measuring the changes in electrical bioimpedance which can be used to derive important hemodynamic variables, it allows the postoperative graft surveillance and early detection atherosclerosis and thrombosis as well as estimate its severity that leads to the vein graft failure.

Base fluid in improving heat transfer for EV car battery

This study examined the effects of base fluid (as coolants) channeling inside the heat exchanger in the process of the increase in thermal conductivity between EV car battery and the heat exchanger. The analysis showed that secondary cooling system by means of water has advantages in improving the heat transfer process and reducing the electric power loss on the form of thermal energy from batteries. This leads to the increase in the efficiency of the EV car battery, hence also positively reflecting the performance of the EV car. The present work, analysis is performed to assess the design and use of heat exchanger in increasing the performance efficiency of the EV car battery. This provides a preface to the use this design for nano-fluids which increase and improve from heat transfer.

Feature Extraction and Optimum Part Deposition Orientation for FDM

Support generation is an essential for Fused Deposition Modeling (FDM) process which is dependent on part deposition orientation. Various part deposition orientation result in formation of different support and non-support features. Present work focuses on extracting the support features containing Externally-Supported Features (ESF) which are able to determine the volume and number of support structure. The methodology proposed in this work uses these information as an input for Artificial Neural Network (ANN) in order to automate the selection of optimum part deposition orientation. The results produced in present methodology can be predicted and are in agreement with the results published earlier.