Intelligent data analysis algorithms on biofeedback signals for estimating emotions

Emotions are an unstoppable and uncontrollable aspect of human reaction to an action. It is a proved mechanism that encompasses variation in emotions due to different acts, expectations, focus to goals, appraisal and resulting reactions. When human body experiences different emotions like fear, anger, sad, happiness then there is also change in different bio-signals which are flowing in human body. These biosignals are: Galvanic skin conductance (GSR), blood volume pulse (BVP), brain waves (EEG), muscle tension, temperature, and respiration. The overall objective of this work is to design and develop a low power, portable, and cost effective embedded system through which we can measure different parameters of autonomic nervous system (GSR/ temperature/Heart rate/EEG) of a person and display it on any of the output devices by using effective algorithm for stress recognition using learning systems. This portable embedded system will be using MSP430F2013 micro controller from Texas Instruments. This system will help measure different parameters such as GSR and BVP. This paper is also discusses an experiment which identifies the stimuli triggering off the emotional state of the person as a result of variations in the bio-signals.

[1]  Justin Dauwels,et al.  Topics in Brain Signal Processing , 2010 .

[2]  Tarik Al-Ani,et al.  Signal Processing and Classification Approaches for Brain-Computer Interface , 2010 .

[3]  Patrick E. McSharry,et al.  A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms , 2004, SPIE International Symposium on Fluctuations and Noise.

[4]  Begoña García Zapirain,et al.  A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , 2012, Sensors.

[5]  Shingo Kuroiwa,et al.  Researches on the emotion measurement system , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[6]  Rolfe SwintonRana El Kaliouby.  MEASURING EMOTIONS THROUGH A MOBILE DEVICE ACROSS BORDERS, AGES, GENDERS AND MORE , 2012 .

[7]  Johannes Wagner,et al.  Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data , 2011, IEEE Transactions on Affective Computing.

[8]  Shyang Chang,et al.  Fractional Brownian Motion in Biomedical Signal Processing, Physiology, and Modern Physics , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.

[9]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .

[10]  Mykola Pechenizkiy,et al.  What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[11]  José M. F. Moura,et al.  Biomedical Signal Processing , 2018, Series in BioEngineering.

[12]  M Senthilmurugan.,et al.  Classification in EEG-Based Brain Computer Interfaces Using Inverse Model , 2011 .

[13]  Armando Barreto,et al.  Stress detection in computer users through non-invasive monitoring of physiological signals. , 2006, Biomedical sciences instrumentation.