Towards an affective aware home (original) (raw)

Affect-aware behaviour modelling and control inside an intelligent environment

The evidence suggests that human actions are supported by emotional elements that complement logic inference in our decision-making processes.In this paper an exploratory study is presented providing initial evidence of the positive effects of emotional information on the ability of intelligent agents to create better models of user actions inside smart-homes. Preliminary results suggest that an agent incorporating valence-based emotional data into its input array can model user behaviour in a more accurate way than agents using no emotion-based data or raw data based on physiological changes. [This version of the paper is a pre-publication version with slightly more data and text]

Framework for an Intelligent Affect Aware Smart Home Environment for Elderly People

International Journal of Recent Trends in Human Computer Interaction (IJHCI), 2019

The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future. Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior, that elderly people face with increasing age. To reduce their financial burden on the world economy and to enhance their quality of life, it is essential to develop technology-based solutions that are adaptive, assistive and intelligent in nature. Intelligent Affect Aware Systems that can not only analyze but also predict the behavior of elderly people in the context of their day to day interactions with technology in an IoT-based environment, holds immense potential for serving as a long-term solution for improving the user experience of elderly in smart homes. This work therefore proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience even before they start engaging in any activity in the given smart home environment. This forecasting of user experience would provide scope for enhancing the same, thereby increasing the assistive and adaptive nature of such intelligent systems. To uphold the efficacy of this proposed framework for improving the quality of life of elderly people in smart homes, it has been tested on three datasets and the results are presented and discussed.

A user-independent real-time emotion recognition system for software agents in domestic environments

Engineering Applications of Artificial Intelligence, 2007

The mystery surrounding emotions, how they work and how they affect our lives has not yet been unravelled. Scientists still debate the real nature of emotions, whether they are evolutionary, physiological or cognitive are just a few of the different approaches used to explain affective states. Regardless of the various emotional paradigms, neurologists have made progress in demonstrating that emotion is as, or more, important than reason in the process of making decisions and deciding actions. The significance of these findings should not be overlooked in a world that is increasingly reliant on computers to accommodate to user needs. In this paper, a novel approach for recognizing and classifying positive and negative emotional changes in real time using physiological signals is presented. Based on sequential analysis and autoassociative networks, the emotion detection system outlined here is potentially capable of operating on any individual regardless of their physical state and emotional intensity without requiring an arduous adaptation or pre-analysis phase. Results from applying this methodology on real-time data collected from a single subject demonstrated a recognition level of 71.4% which is comparable to the best results achieved by others through off-line analysis. It is suggested that the detection mechanism outlined in this paper has all the characteristics needed to perform emotion recognition in pervasive computing.

Towards Truly Affective AAL Systems

Enhanced Living Environments, 2019

Affective computing is a growing field of artificial intelligence. It focuses on models and strategies for detecting, obtaining, and expressing various affective states, including emotions, moods, and personality related attributes. The techniques and models developed in affective computing are applicable to various affective contexts, including Ambient Assisted Living. One of the hypotheses for the origin of emotion is that the primary purpose was to regulate social interactions. Since one of the crucial characteristics of Ambient Assisted Living systems is supporting social contact, it is unthinkable to build such systems without considering emotions. Moreover, the emotional capacity needed for Ambient Assisted Living systems exceeds simple user emotion detection and showing emotion expressions of the system. In addition, emotion generation and emotion mapping on rational thinking and behavior of a system should be considered. The chapter discusses the need and requirements for these processes in the context of various application domains of Ambient Assisted Living, i.e., healthcare, mobility, education, and social interaction.

Towards a robust real-time emotion detection system for intelligent buildings

The last few years have witnessed an increasing interest from computer scientists in the role emotions could play in the adaptability of artificially intelligent mechanisms. Evidence from neurologists suggests that affective states are crucial in the interaction between an individual and the environment. Furthermore, emotions often dominate our actions and some times override reasoning in the process of making decisions. In this paper an analysis of the use of Autoassociative Neural Networks (AANN) in the context of real-time physiological emotion detection for intelligent inhabited environments (IIE) is presented. Two main studies were undertaken: On one hand the effects of altered physiological responses stemmed from various degrees of emotive intensity and on the other hand the possible consequences of physical arousal not related to emotional expressiveness It is argued that the use of AANN contributes to an improved separation of emotional classes and a more accurate recognition of affective states in individuals with a varying degree of emotional responsiveness. It is also postulated that AANNs robustness is not affected by physiological disturbances associated with physical activities thus setting the basis for emotion recognition in real-life scenarios.

Affective interaction in smart environments

We present a concept where the smart environments of the future will be able to provide ubiquitous affective communication. All the surfaces will become interactive and the furniture will display emotions. In particular, we present a first prototype that allows people to share their emotional states in a natural way. The input will be given through facial expressions and the output will be displayed in a context-aware multimodal way. Two novel output modalities are presented: a robotic painting that applies the concept of affective communication to the informative art and an RGB lamp that represents the emotions remaining in the user's peripheral attention. An observation study has been conducted during an interactive event and we report our preliminary findings in this paper.

The Effect of Emotional Speech on a Smart-Home Application

2008

The present work studies the effect of emotional speech on a smart-home application. Specifically, we evaluate the recognition performance of the automatic speech recognition component of a smart-home dialogue system for various categories of emotional speech. The experimental results reveal that word recognition rate for emotional speech varies significantly across different emotion categories.

Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition

Advances in Human-Computer Interaction

Nowadays, much research attention is focused on human–computer interaction (HCI), specifically in terms of biosignal, which has been recently used for the remote controlling to offer benefits especially for disabled people or protecting against contagions, such as coronavirus. In this paper, a biosignal type, namely, facial emotional signal, is proposed to control electronic devices remotely via emotional vision recognition. The objective is converting only two facial emotions: a smiling or nonsmiling vision signal captured by the camera into a remote control signal. The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. In terms of the smiling recognition, GENKl-4K database is exploited to train a model, which is built in the following sequenced steps: real-time video, snapshot image, preprocessing, face detection, feature extraction using HOG, and then finally SVM for the classification. The achieve...

Magic mirror table for social-emotion alleviation in the smart home

IEEE Transactions on Consumer Electronics, 2012

In this paper, we propose a prototype of smart furniture for the smart home-a magic mirror table. The proposed system has a camera to capture the viewer's facial expression. By analyzing the expressions, the system is able to determine the emotion of the viewer. If the viewer is in a negative emotion, the system then speaks positive sentences and plays the viewer's favorite music to alleviate his/her emotion. The experimental results confirm that the system is able to relieve the sad mood of the viewer. In addition, the proposed system can serve as a calendar for event reminding. 1

eMuu: an embodied emotional character for the ambient intelligent home

2002

Many companies, universities and research institutes are working on the home of the future. Besides Microsoft and IBM, Philips Research is one of the key players and made recently a considerable step forward by opening a first prototype, called “Home Lab”(Aarts, 2002). One of the central concepts in the idea for the HomeLab is Ambient Intelligence (see box 1)(Aarts, Harwig, & Schuurmans, 2001). One of the key components of ambient intelligence, as described above, is the natural interaction between the home and the user.