Towards affective sensing

Affective Computing: Focus on Emotion …, 2008

Affective Computing and …, 2007

Lecture Notes in Electrical Engineering, 2018

Affective computing (AC) is an emerging research direction to deal with the great challenge of creating emotional intelligence for a machine. Affective computing is a cross-disciplinary research knowledge that integrates recognition, interpretation, and simulation of human emotion into a system. This article describes the design of multimodal perception of affective computing system. Our multimodal physiological channels include facial expression recognition, heart rate monitoring, blood oxygen level (SpO2), skin conductance response (SCR), and electroencephalogram (EEG) signals for building our affective computing system. To solve the various data sampling problem, we developed a concurrent control integration mechanism to automatically average all the sensors' data into the same data sampling rate (one record per second) and rearrange all the data into the same time. We believed the proposed system design is benefit for helping researchers in collecting and integrating experiment data in affective computing area.

… on Multimedia and Expo, 2005. ICME …, 2005

Detecting emotional responses in multimedia environments is an academically and technologically challenging research issue. In the domain of Affective Computing, from non-interactive and static stimuli (e.g. affective image) to highly interactive and dynamic environments (affective virtual realities), researchers have employed a wide range of affective stimuli to measure and interpret human psychological and physiological emotional behaviours. Various psychophysiological parameters (e.g. Electroencephalography, Galvanic Skin Response, Heart Rate, etc.) have been employed and investigated, in order to detect and quantify human affective states. In this paper, we present a detailed literature review of over 33 affective computing studies, undertaken since 1993. All aspects of these studies (stimuli type, pre-processing, windowing, features, classification technique, etc.) have been reported in detail. We believe that this paper not only summarises the breadth of research over the past...

Affective and human-centered computing have attracted an abundance of attention during the past years, mainly due to the abundance of environments and applications able to exploit and adapt to multimodal input from the users. The combination of facial expressions with prosody information allows us to capture the users’ emotional state in an unintrusive manner, relying on the best performing modality in cases where one modality suffers from noise or bad sensing conditions. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences, where input is taken from nearly real world situations, contrary to controlled recording conditions of audiovisual material. Recognition is performed via a recurrent neural network, whose short term memory and approximation capabilities cater for modeling dynamic events in facial and prosodic expressivity. This approach also differs from existing work in that it models user expressivity using a dimensional representation, instead of detecting discrete ‘universal emotions’, which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels. Results show that in turns lasting more than a few frames, recognition rates rise to 98%.

International Journal of Advanced Computer Science and Applications, 2013

Emotion is assuming increasing importance in human computer interaction (HCI), in general, with the growing feeling that emotion is central to human communication and intelligence. Users expect not just functionality as a factor of usability, but experiences, matched to their expectations, emotional states, and interaction goals. Endowing computers with this kind of intelligence for HCI is a complex task. It becomes more complex with the fact that the interaction of humans with their environment (including other humans) is naturally multimodal. In reality, one uses a combination of modalities and they are not treated independently. In an attempt to render HCI more similar to human-human communication and enhance its naturalness, research on multiple modalities of human expressions has seen ongoing progress in the past few years. As compared to unimodal approaches, various problems arise in case of multimodal emotion recognition especially concerning fusion architecture of multimodal information. In this paper we will be proposing a rule based hybrid approach to combine the information from various sources for recognizing the target emotions. The results presented in this paper shows that it is feasible to recognize human affective states with a reasonable accuracy by combining the modalities together using rule based system.

Internet Imaging VI, 2005

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and pshysiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

International Journal of Human-Computer Studies, 2003

A number of researchers around the world have built machines that recognize, express, model, communicate, and respond to emotional information, instances of ''affective computing.'' This article raises and responds to several criticisms of affective computing, articulating state-of-the art research challenges, especially with respect to affect in humancomputer interaction.

Sensors, 2020

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participan...