A testament to the efficacy of JERI is that in recent years, several similar annotation setups have been presented 25, 30. To address these drawbacks, we developed a novel Joystick-based Emotion Reporting Interface (JERI) that facilitates simultaneous annotation of valence and arousal 16, 27, 28, 29. These being, that separate annotation of valence and arousal does not account for the inherent relationship between them 23, 25, and that mouse-based annotation tools are generally less ergonomic than joysticks 22, 23, 24, 26. In recent years, both these aspects have been reported to have major drawbacks 22, 23, 24. Second, in all datasets except SEWA, that uses a joystick, mouse-based annotation tools were used. First, the two dimensions of the Circumplex model (i.e., valence and arousal) were annotated separately. The annotation strategy used in these datasets have the following common aspects. Principal among these are the DEAP 18, SEMAINE 19, RECOLA 20, DECAF 21 and SEWA 22. To address this issue, several (uni- and multi-modal) datasets that incorporate continuous annotation have been developed. This makes undertaking these steps a fairly time-consuming and expensive exercise. Notwithstanding these efforts at improving the annotation process, a major impediment in the AC pipeline is that steps (i) and (ii) require direct human involvement in the form of subjects from whom these indicators and annotations are acquired 8, 17. Therefore, continuous annotation based on dimensional models is preferred and several annotation tools for undertaking the same have been developed 14, 15, 16. However, these approaches were insufficient for defining the intensity 6, 8, 11 and accounting for the time-varying nature 13 of emotional experiences. Traditionally, approaches based on discrete emotional categories were commonly used.
Second, on the basis of the emotion-model used, i.e., either discrete emotion categories (e.g., joy, anger, etc.) or dimensional models (e.g., the Circumplex model 12).
First, on the kind of annotation scale employed, i.e., either discrete or continuous. Similarly, the approaches to step (ii) vary along the following two main aspects. For example, during step (i) different modalities like physiological signals 5, 7, 8, speech 9 and facial-expressions 10, 11 can be acquired. For undertaking steps (i) and (ii) several different strategies are used. To overcome this limitation, the standard AC processing pipeline 6 involves: (i) acquiring measurable indicators of human emotions, (ii) acquiring subjective annotations of internal emotions, and (iii) modelling the relation between these indicators and annotations to make predictions about the emotional state of the user. Addressing this shortcoming is the aim of the interdisciplinary field of Affective Computing (AC, also known as Emotional AI), that focuses on developing machines capable of recognising, interpreting and adapting to human emotions 3, 4.Ī major hurdle in developing these affective machines is the internal nature of emotions that makes them inaccessible to external systems 5. These advancements in interpreting explicit human intent, while highly commendable, often overlook implicit aspects of human–human interactions and the role emotions play in them. For example, services like customer support and patient care, that were till recently only accessible through human–human interaction, can nowadays be offered through AI-enabled conversational chatbots 1 and robotic daily assistants 2, respectively. The field of Artificial Intelligence (AI) has rapidly advanced in the last decade and is on the cusp of transforming several aspects of our daily existence. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli.
#End screen annotation problems skin
In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were obtained from ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature sensors. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos.
As a result, proper emotion assessment remains a problematic issue. Discrete, indirect, post-hoc recordings are therefore the norm. In research, a direct and real-time inspection in realistic settings is not possible. From a computational viewpoint, emotions continue to be intriguingly hard to understand.