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14 avril 2014
- handle: 10670/1.llh162
- hal: hal-01397139
- doi: 10.1007/978-3-642-55032-4_1
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-55032-4_1
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Emotion Recognition Affective Computing Context Pattern Recognition Machine Learning Reality Mining Intelligent SystemsSujets proches
Natural science Sciences Science of science Large data sets Data sets, LargeCiter ce document
Hajar Mousannif et al., « The Human Face of Mobile », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-642-55032-4_1
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Résumé
As the landscape around Big data continues to exponentially evolve, the « big » facet of Big data is no more number one priority of researchers and IT professionals. The race has recently become more about how to sift through torrents of data to find the hidden diamond and engineer a better, smarter and healthier world. The ease with which our mobile captures daily data about ourselves makes it an exceptionally suitable means for ultimately improving the quality of our lives and gaining valuable insights into our affective, mental and physical state. This paper takes the first exploratory step into this direction by using the mobile to process and analyze the “digital exhaust” it collects to automatically recognize our emotional states and accordingly respond to them in the most effective and “human” way possible. To achieve this we treat all technical, psycho-somatic, and cognitive aspects of emotion observation and prediction, and repackage all these elements into a mobile multimodal emotion recognition system that can be used on any mobile device.