Internet of Emotions: Emotion Management Using Affective Computing

The many advantages of increase in Human Machine Interaction are obvious but it has also led to issues such as emotional imbalance, depression, reduction in interpersonal communication etc. Internet of Emotions can be broadly categorized as internet based technologies which aim to mitigate these problems and facilitate better Human to Human interaction in real world. IoE can be defined as an ecosystem where emotion packets travel via internet to manage user’s real time experience. We propose a system which will detect emotional state of the user, categorize it and actuate outer net elements to manage the emotion of the user. Detailed algorithm is given which includes use of the passive sensors, smartphone, big data analytics and machine learning. The framework is further explained with example of stress management. The proposed system based on affective computing will play a vital role in development of products and platforms which emphasises user involvement.

[1]  Ling Guan,et al.  A neural network approach for human emotion recognition in speech , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[2]  Cynthia Breazeal,et al.  Improved human-robot team performance using Chaski, A human-inspired plan execution system , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[3]  Geoffrey A. Hollinger,et al.  Design of a Social Mobile Robot Using Emotion-Based Decision Mechanisms , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Cecilia Mascolo,et al.  EmotionSense: a mobile phones based adaptive platform for experimental social psychology research , 2010, UbiComp.

[5]  Peter Robinson,et al.  Generalization of a Vision-Based Computational Model of Mind-Reading , 2005, ACII.

[6]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.

[7]  Po-Ming Lee,et al.  The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli , 2015, PloS one.

[8]  J. Gratch,et al.  The Oxford Handbook of Affective Computing , 2014 .

[9]  Cynthia Breazeal,et al.  Emotion and sociable humanoid robots , 2003, Int. J. Hum. Comput. Stud..

[10]  Rosalind W. Picard.  Affective Computing for Future Agents , 2000, CIA.

[11]  Daniel McDuff,et al.  Wearable ESM: differences in the experience sampling method across wearable devices , 2016, MobileHCI.

[12]  Nicu Sebe,et al.  Emotion Recognition Based on Joint Visual and Audio Cues , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  S. Saxena,et al.  Depression: a global public health concern , 2012 .

[14]  David Ellis,et al.  Stress Detection Using Wearable Physiological and Sociometric Sensors , 2017, Int. J. Neural Syst..

[15]  Shen Furao,et al.  An enhanced self-organizing incremental neural network for online unsupervised learning , 2007, Neural Networks.

[16]  Rosalind W. Picard.  The Promise of Affective Computing , 2015 .

[17]  Lars Kai Hansen,et al.  Good Friends, Bad News - Affect and Virality in Twitter , 2011, ArXiv.

[18]  Peter Robinson,et al.  Mind reading machines: automated inference of cognitive mental states from video , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).