15,870 research outputs found
Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
Despite significant recent advances in the field of head pose estimation and
facial expression recognition, raising the cognitive level when analysing human
activity presents serious challenges to current concepts. Motivated by the need
of generating comprehensible visual representations from different sets of
data, we introduce a system capable of monitoring human activity through head
pose and facial expression changes, utilising an affordable 3D sensing
technology (Microsoft Kinect sensor). An approach build on discriminative
random regression forests was selected in order to rapidly and accurately
estimate head pose changes in unconstrained environment. In order to complete
the secondary process of recognising four universal dominant facial expressions
(happiness, anger, sadness and surprise), emotion recognition via facial
expressions (ERFE) was adopted. After that, a lightweight data exchange format
(JavaScript Object Notation-JSON) is employed, in order to manipulate the data
extracted from the two aforementioned settings. Such mechanism can yield a
platform for objective and effortless assessment of human activity within the
context of serious gaming and human-computer interaction.Comment: 8th Computer Science and Electronic Engineering, (CEEC 2016),
University of Essex, UK, 6 page
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
An Intervening Ethical Governor for a Robot Mediator in Patient-Caregiver Relationships
© Springer International Publishing AG 2015DOI: 10.1007/978-3-319-46667-5_6Patients with Parkinson’s disease (PD) experience challenges when interacting with
caregivers due to their declining control over their musculature. To remedy those challenges, a
robot mediator can be used to assist in the relationship between PD patients and their caregivers.
In this context, a variety of ethical issues can arise. To overcome one issue in particular,
providing therapeutic robots with a robot architecture that can ensure patients’ and caregivers’
dignity is of potential value. In this paper, we describe an intervening ethical governor for a
robot that enables it to ethically intervene, both to maintain effective patient–caregiver
relationships and prevent the loss of dignity
An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder
This document is the Accepted Manuscript version of the following article: Amanda K. Ludlow, Eleanor Chadwick, Alice Morey, Rebecca Edwards, and Roberto Gutierrez, ‘An exploration of sarcasm detection in children with Attention Deficit Hyperactivity Disorder’, Journal of Communication Disorders, Vol. 70: 25-34, November 2017. Under embargo. Embargo end date: 31 October 2019. The Version of Record is available at doi: https://doi.org/10.1016/j.jcomdis.2017.10.003.The present research explored the ability of children with ADHD to distinguish between sarcasm and sincerity. Twenty-two children with a clinical diagnosis of ADHD were compared with 22 age and verbal IQ matched typically developing children using the Social Inference–Minimal Test from The Awareness of Social Inference Test (TASIT, McDonald, Flanagan, & Rollins, 2002). This test assesses an individual’s ability to interpret naturalistic social interactions containing sincerity, simple sarcasm and paradoxical sarcasm. Children with ADHD demonstrated specific deficits in comprehending paradoxical sarcasm and they performed significantly less accurately than the typically developing children. While there were no significant differences between the children with ADHD and the typically developing children in their ability to comprehend sarcasm based on the speaker’s intentions and beliefs, the children with ADHD were found to be significantly less accurate when basing their decision on the feelings of the speaker, but also on what the speaker had said. Results are discussed in light of difficulties in their understanding of complex cues of social interactions, and non-literal language being symptomatic of children with a clinical diagnosis of ADHD. The importance of pragmatic language skills in their ability to detect social and emotional information is highlighted.Peer reviewe
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
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