A New Approach to Emotion Assessment Based on Biometric Data

Teixeira, Jorge; Vinhas, Vasco; Oliveira, Eugénio; Reis, Luís Paulo

doi:10.1109/wiiat.2008.253

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Laster has been used for face and gesture recognition for HCI (Reilly 1998). Another popular method for emotion recognition is biometric data (Teixeira et al 2008). Dedicated systems often facilitate the challenge of emotion detection Sheldon 2001;Vick and Ikehara 2003).…”

Section: Emotional Statesmentioning

confidence: 99%

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Bibri

2015

Atlantis Ambient and Pervasive Intelligence

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There exist a vast range of AmI architectures that essentially aim to provide the appropriate infrastructure for AmI systems. Typically, they include many sensors of diverse types, information processing systems or computing devices where modeling and reasoning occur, and actuators through which the system acts, reacts, or pre-acts in the physical world. There are many permutations of enabling technologies and computational processes of AmI, which result in many heterogeneous components (devices and systems and associated software applications) which have to interconnect and communicate seamlessly across disparate networks as part of vast architectures enabling context awareness, machine learning and reasoning, ontological representation and reasoning, and adaptation of services. The sensors are basically utilized to acquire the contextual data needed for the context recognition process-that is, observed information as input for AmI systems to analyze, model, and understand the user's context, so to undertake in a knowledgeable manner actions accordingly. Sensor technology is thus a key enabler of context awareness functionality in AmI systems. Specifically, to acquire, fuse, process, propagate, interpret, and reason about context data in the AmI space to support adaptation of services requires using dedicated sensors and signal and data processing techniques, in addition to sophisticated context recognition algorithms based on a wide variety of methods and techniques for modeling and reasoning. The challenge of incorporating context awareness functionality in the AmI service provision system lies in the complexity associated with sensing, learning, capturing, representing, processing, and managing context information.Context-aware systems are increasingly maturing and rapidly proliferating, spanning a variety of application domains, owing to recent advances in capture technologies, the diversity of recognition approaches, multi-senor fusion techniques, and sensor networks, as well as pattern recognition algorithms and representation and reasoning techniques. Numerous recognition approaches have been developed 129 and studied, and a wide variety of related projects have been carried out within various domains of context awareness. Most of early research work on context awareness focused on user's physical context, which can be inferred using different types of sensing facilities, including stereo-type cameras, RFID, and smart devices. While most attempts to use context awareness within AmI environments were centered on the physical elements of the environment or users, in recent years, research in the area of context recognition has shifted the focus to human elements of context, such as emotional states, cognitive states, physiological states, activities, and behaviors. This has led to the development and employment of different recognition methods, mainly vision-based, multisensory-based, and sensor-based context and activity recognition approaches. Furthermore, investigating methods for context recognition ...

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Section: Emotional Statesmentioning

confidence: 99%

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Bibri

2015

Atlantis Ambient and Pervasive Intelligence

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“…The interest in this field is usually focused on recognizing the human emotions in humancomputer interaction. In the popular methods, emotion recognition is focused on: facial expressions (Hager et al, 2002), voice (Kang et al, 2000) or biometric data (Teixeira et al, 2008). However, these methods, based on a behavioural approach, ignore the semantic and pragmatic context of emotions.…”

Section: Implementing Emotional Intelligence In Conversational Agentsmentioning

confidence: 99%

“…This leads to the need of applying contextual analysis to emotion processing. Furthermore, although it was proved that affective states should be analysed as emotion specific (Lerner & Kelter, 2000), most of the behavioural approach methods simply classify them to opposing pairs such as joy-anger, or happiness-sadness (Teixeira et al, 2008). A positive change in this tendency can be seen in text mining and information extraction approaches to emotion estimation (Tokuhisa et al, 2008;Ptaszynski et al, 2009b).…”

Section: Implementing Emotional Intelligence In Conversational Agentsmentioning

confidence: 99%

Towards Socialized Machines: Emotions and Sense of Humour in Conversational Agents

Ptaszyński¹,

Dybała²,

Higuhi³

et al. 2010

Web Intelligence and Intelligent Agents

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“…The interest in such research is usually focused on recognising the emotions of users in human-computer interaction. In the most popular methods the emotions are recognised from: facial expressions (Hager et al, 2002), voice (Nwe et al, 2003) or biometric data (Teixeira et al, 2008). However these methods, usually based on behavioural approaches, ignore the semantic context of emotions.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to the need of applying contextual analysis to emotion processing. Furthermore, although recent discoveries prove that affective states should be analysed as emotion-specific rather than divided simply into positive or negative valence (Lerner and Kelter, 2000), most of the behavioural approach methods are incapable to distinguish emotions in more subtle manner than the two-part classification to pairs like joy-anger, or happiness-sadness (see Teixeira et al, 2008). Furthermore, biometric methods, like functional Magnetic Resonance Imaging (fMRI) or Electroencephalography (EEG), are usually laborious, time consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

Contextual affect analysis: a system for verification of emotion appropriateness supported with Contextual Valence Shifters

Ptaszyński

Dybała

Shi

et al. 2010

IJBM

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This paper presents a novel method for estimating speaker's affective states based on two contextual features: valence shifters and appropriateness. Firstly, a system for affect analysis is used to recognise specific types of emotions. We improve the baseline system with the analysis of Contextual Valence Shifters (CVS), which determine the semantic orientation of emotive expressions. Secondly, a web mining technique is used to verify the appropriateness of the recognised emotions for the particular context. Verification of contextual appropriateness of emotions is the next step towards implementation of Emotional Intelligence Framework in machines. The proposed method is evaluated using two conversational agents.

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A New Approach to Emotion Assessment Based on Biometric Data

Cited by 8 publications

References 8 publications

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Towards Socialized Machines: Emotions and Sense of Humour in Conversational Agents

Contextual affect analysis: a system for verification of emotion appropriateness supported with Contextual Valence Shifters

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