{"id":9367,"date":"2019-03-12T00:00:00","date_gmt":"2019-03-11T23:00:00","guid":{"rendered":"https:\/\/www.ilc.cnr.it\/events\/seminar-12-03-2019\/"},"modified":"2023-03-03T15:23:08","modified_gmt":"2023-03-03T14:23:08","slug":"seminar-12-03-2019","status":"publish","type":"events","link":"https:\/\/www.ilc.cnr.it\/en\/events\/seminar-12-03-2019\/","title":{"rendered":"Seminar 12\/03\/2019"},"content":{"rendered":"
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Title:<\/div>\n
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Emotion Detection Using Noninvasive Low-cost Sensors<\/div>\n<\/div>\n<\/div>\n
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Date:<\/div>\n
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12\/03\/2019<\/span><\/div>\n<\/div>\n<\/div>\n
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Time:<\/div>\n
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11:00<\/div>\n<\/div>\n<\/div>\n
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Town:<\/div>\n
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Pisa<\/div>\n<\/div>\n<\/div>\n
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Venue:<\/div>\n
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ILC-CNR – Aula Seminari IBF SG 5<\/div>\n<\/div>\n<\/div>\n
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Description:<\/div>\n
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Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare and software development.<\/div>\n
Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations.<\/div>\n
We investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low-cost EEG, EMG, and GSR sensors.<\/div>\n
We report the results of an empirical study involving 19 subjects in a laboratory setting for emotion elicitation.<\/div>\n
We achieved state-of-the-art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.<\/div>\n
Furthermore, we will discuss our ongoing work on the recognition of affective and cognitive states of software engineers during their daily programming tasks.<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Speaker(s):<\/div>\n
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Nicole Novielli, University of Bari \u201cA. Moro\u201d, Department of Computer Science, Collaborative Development Group<\/div>\n<\/div>\n<\/div>\n
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Web:<\/div>\n
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http:\/\/collab.di.uniba.it\/nicole<\/a><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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