Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Exploring the design of interactive smart textiles for emotion regulation

The present study aims to investigate the design of interactive textiles for emotion regulation. In this work we proposed a design which allows users to visualize their physiological data and help regulate their emotions. We used the Research through Design method to explore how physiological data could be represented in four different interactive textiles and how movement-based interaction could be designed to support users’ understanding and regulation of their emotional state. After an initial user interview evaluation with several textile prototypes, light and vibration were selected as modalities within the biofeedback-based interaction. A smart interactive shawl that reacts to changes in emotional arousal was designed to help the users know their emotion and adjust it, if necessary, with the support of electrodermal activity sensor and pressure-based sensors. The results of the second study showed that the smart shawl could help the user to visualize their emotions and reduce their stress level by interacting with it. © 2020, Springer Nature Switzerland AG

Psychophysiology in games

Psychophysiology is the study of the relationship between psychology and its physiological manifestations. That relationship is of particular importance for both game design and ultimately gameplaying. Players’ psychophysiology offers a gateway towards a better understanding of playing behavior and experience. That knowledge can, in turn, be beneficial for the player as it allows designers to make better games for them; either explicitly by altering the game during play or implicitly during the game design process. This chapter argues for the importance of physiology for the investigation of player affect in games, reviews the current state of the art in sensor technology and outlines the key phases for the application of psychophysiology in games.The work is supported, in part, by the EU-funded FP7 ICT iLearnRWproject (project no: 318803).peer-reviewe

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

Towards estimating computer users' mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse

Developing Multimodal Intelligent Affective Interfaces For Tele-Home Health Care

Accounting for a patient\u27s emotional state is integral in medical care. Tele-health research attests to the challenge clinicians must overcome in assessing patient emotional state when modalities are limited (J. Adv. Nurs. 36(5) 668). The extra effort involved in addressing this challenge requires attention, skill, and time. Large caseloads may not afford tele-home health-care (tele-HHC) clinicians the time and focus necessary to accurately assess emotional states and trends. Unstructured interviews with experienced tele-HHC providers support the introduction of objective indicators of patients\u27 emotional status in a useful form to enhance patient care. We discuss our contribution to addressing this challenge, which involves building user models not only of the physical characteristics of users - in our case patients - but also models of their emotions. We explain our research in progress on Affective Computing for tele-HHC applications, which includes: developing a system architecture for monitoring and responding to human multimodal affect and emotions via multimedia and empathetic avatars; mapping of physiological signals to emotions and synthesizing the patient\u27s affective information for the health-care provider. Our results using a wireless non-invasive wearable computer to collect physiological signals and mapping these to emotional states show the feasibility of our approach, for which we lastly discuss the future research issues that we have identified. © 2003 Elsevier Science Ltd. All rights reserved

Abandon et clôture des fours de potiers : un geste systématique ?

Analyser les gestes structurés qui reflètent des possibles rites et croyances en contexte artisanal. Est-ce que les potiers gallo-romains installent et clôturent leurs structures artisanales accompagnés de rites particuliers et comment les identifier