Affective computing
This dataset includes acceleration data measured by 36 participants across 154 Tactons (i.e., Tactile Icons). We used three iOS smartphones (iPhone 13 mini, iPhone 14, and iPhone 11 Pro Max) by Apple Inc. to collect acceleration data as well as sensory and emotional ratings of Tactons on various consumer phones. These phones varied in size and mass: iPhone 13 mini (64.2 x 131.5 x 7.65 mm, 141 g), iPhone 14 (71.5 x 146.7 x 7.8 mm, 172 g), and iPhone 11 Pro Max (77.8 x 158.0 x 8.1 mm, 226 g).
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10 soccer supporters gathered to watch a live broadcasted Premier League
match between Liverpool and Manchester United (4 - 0) on 19th of March 2022, all
equipped with wrist-worn accelerometers. All participants were aware of the purpose of this experiment and consented to participate
by attendance at the event, and by wearing the accelerometer. No personally sensitive
information was collected, all data is fully anonymised following the GDPR guidelines
and all procedures were in accordance with the recommendations of the data protection
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The ability to perceive human facial emotions is an essential feature of various multi-modal applications, especially in the intelligent human-computer interaction (HCI) area. In recent decades, considerable efforts have been put into researching automatic facial emotion recognition (FER). However, most of the existing FER methods only focus on either basic emotions such as the seven/eight categories (e.g., happiness, anger and surprise) or abstract dimensions (valence, arousal, etc.), while neglecting the fruitful nature of emotion statements.
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The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
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