By Hugo Plácido da Silva, Andreas Holzinger, Stephen Fairclough, Dennis Majoe
This ebook constitutes the complaints of the 1st foreign convention on Physiological Computing platforms, PhyCS 2014, held in Lisbon, Portugal, in January 2014. the ten papers provided during this quantity have been conscientiously reviewed and chosen from fifty two submissions. they're geared up in topical sections named: methodologies and strategies; units; purposes; and human factors.
Read or Download Physiological Computing Systems: First International Conference, PhyCS 2014, Lisbon, Portugal, January 7-9, 2014, Revised Selected Papers PDF
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Extra resources for Physiological Computing Systems: First International Conference, PhyCS 2014, Lisbon, Portugal, January 7-9, 2014, Revised Selected Papers
Example text
The method maximizes the ratio of between-class variance to the within-class variance, permitting to maximize class separability. More formally, LDA finds the eigenvectors φi that solve: Bφi − λWφi = 0 (1) where B is the between-class scatter matrix and W is the within-class scatter matrix. Once a sample xj is projected on the new space provided by the eigenvectors, the class kˆ corresponding to the projection y j is chosen according to [9]: kˆ = argmax p(k|y j ) = argmax −d2k (y j ) k k (2) where d2k (·) is the generalized squared distance function: ln |Σk | − ln p(k) (3) 2 where Σ k is the covariance matrix for the class k and p(k) is the a-priori probability of the class k: d2k (y) = (y − μj )T Σk−1 (y − μj ) + nk K i=1 (4) ni where nk is the number of samples belonging to the class k, and K is the number of classes.
In the early 1990s, computer scientists broadened this idea to create a new field of research: physiological computing [2]. The goal of physiological computing is to translate bioelectrical signals from the human nervous system into computational data. A wide range of applications in human-computer interactions, from brain-computer interactions to affective computing, require the recording and processing of the user’s nervous system activity. This chapter focuses on one subfield of physiological computing that aims to connect physiological measures with psychological states.
Le emozioni. Edizioni Unicopli, Milano (2002) 2. : The Voice of Emotions. Angeli, Milano (1997) 3. : Recognition of vocal emotions from acoustic profile. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics (2012) 4. : Definizione ed annotazione prosodica di un database di parlato-letto usando il formalismo ToBI. In: Proceedings of Il Parlato Italiano, Napoli, Italy, February 2003 5. : Il lessico emotivo nel decoding delle espressioni facciali.