Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Detecting User Engagement in Everyday Conversations

This paper presents a novel application of speech emotion recognition: estimation of the level of conversational engagement between users of a voice communication system. We begin by using machine learning techniques, such as the support vector machine (SVM), to classify users' emotions as expressed in individual utterances. However, this alone fails to model the temporal and interactive aspects of conversational engagement. We therefore propose the use of a multilevel structure based on coupled hidden Markov models (HMM) to estimate engagement levels in continuous natural speech. The first level is comprised of SVM-based classifiers that recognize emotional states, which could be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM then uses these emotional states as input, estimating users' engagement in conversation by decoding the internal states of the HMM. We report experimental results obtained by applying our algorithms to the LDC Emotional Prosody and CallFriend speech corpora.Comment: 4 pages (A4), 1 figure (EPS

Tactile modulation of emotional speech samples

Copyright © 2012 Katri Salminen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedTraditionally only speech communicates emotions via mobile phone. However, in daily communication the sense of touch mediates emotional information during conversation. The present aim was to study if tactile stimulation affects emotional ratings of speech when measured with scales of pleasantness, arousal, approachability, and dominance. In the Experiment 1 participants rated speech-only and speech-tactile stimuli. The tactile signal mimicked the amplitude changes of the speech. In the Experiment 2 the aim was to study whether the way the tactile signal was produced affected the ratings. The tactile signal either mimicked the amplitude changes of the speech sample in question, or the amplitude changes of another speech sample. Also, concurrent static vibration was included. The results showed that the speech-tactile stimuli were rated as more arousing and dominant than the speech-only stimuli. The speech-only stimuli were rated as more approachable than the speech-tactile stimuli, but only in the Experiment 1. Variations in tactile stimulation also affected the ratings. When the tactile stimulation was static vibration the speech-tactile stimuli were rated as more arousing than when the concurrent tactile stimulation was mimicking speech samples. The results suggest that tactile stimulation offers new ways of modulating and enriching the interpretation of speech.Peer reviewe

Emotions Detection based on a Single-electrode EEG Device

The study of emotions using multiple channels of EEG represents a widespread practice in the field of research related to brain computer interfaces (Brain Computer Interfaces). To date, few studies have been reported in the literature with a reduced number of channels, which when used in the detection of emotions present results that are less accurate than the rest. To detect emotions using an EEG channel and the data obtained is useful for classifying emotions with an accuracy comparable to studies in which there is a high number of channels, is of particular interest in this research framework. This article uses the Neurosky Maindwave device; which has a single electrode to acquire the EEG signal, Matlab software and IBM SPSS Modeler; which process and classify the signals respectively. The accuracy obtained in the detection of emotions in relation to the economic resources of the hardware dedicated to the acquisition of EEG signal is remarkable

Can small be beautiful? assessing image resolution requirements for mobile TV

Mobile TV services are now being offered in several countries, but for cost reasons, most of these services offer material directly recoded for mobile consumption (i.e. without additional editing). The experiment reported in this paper, aims to assess the image resolution and bitrate requirements for displaying this type of material on mobile devices. The study, with 128 participants, examined responses to four different image resolutions, seven video encoding bitrates, two audio bitrates and four content types. The results show that acceptability is significantly lower for images smaller than 168×126, regardless of content type. The effect is more pronounced when bandwidth is abundant, and is due to important detail being lost in the smaller screens. In contrast to previous studies, participants are more likely to rate image quality as unacceptable when the audio quality is high