Using Different Information Channels for Affect-Aware Video Games - A Case Study

Szwoch, Mariusz; Szwoch, Wioleta

doi:10.1007/978-3-030-03658-4_13

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In recent years, there has been increasing interest in the automatic recognition of emotions and taking into account the emotional state of a person in various applications in many fields. Solutions implementing the automated recognition of emotions are used, among others, in marketing, systems monitoring driver tiredness, e-learning, healthcare, education [6], entertainment [7,8], software engineering [9,10], etc. Many different approaches to emotion recognition have been developed, which use more and more advanced algorithms achieving increasing effectiveness.…”

Section: Emotion Recognitionmentioning

confidence: 99%

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

Kołakowska

Szwoch

2020

Sensors

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In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

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Section: Emotion Recognitionmentioning

confidence: 99%

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

Kołakowska

Szwoch

2020

Sensors

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“…Emotion recognition is an emerging research area due to its numerous applications in our daily life. Applications include areas such as developing models for inspecting driver emotions [ 4 ], health care [ 5 , 6 ], software engineering [ 7 ] and entertainment [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

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“…Emotion recognition is an emerging research area due to its numerous applications in our daily life. Applications include areas such as developing models for inspecting driver emotions [1], health care [2] [3], software engineering [4] and entertainment [5].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physiological Signal Based Emotion Recognition

Ahmad¹,

Khan²

2022

Preprint

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Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.

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Using Different Information Channels for Affect-Aware Video Games - A Case Study

Cited by 4 publications

References 11 publications

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

A Survey on Physiological Signal-Based Emotion Recognition

A Survey on Physiological Signal Based Emotion Recognition

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