Affective interaction: Using emotions as a user interface in games

Sekhavat, Yoones A.; Sisi, Milad Jafari; Roohi, Samad

doi:10.1007/s11042-020-10006-4

Cited by 15 publications

(8 citation statements)

References 65 publications

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“…For a long time, the shortage of multimodal affective databases limited the amount of works that could be done in this field, even though some multimodal databases have appeared in the last years (e.g., DEAP [36], RECOLA [57], BP4D+ [73]). There is also a problem with the use of prepared datasets, and it is that models trained with these data do not perform well in spontaneous environments [14,59,52], although there are proposals to fix this issue [33,37,73]. & No context information being considered.…”

Section: Research Challenges Of Multimodal Emotion Recognition Systemsmentioning

confidence: 99%

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Building a three-level multimodal emotion recognition framework

García-García¹,

Lozano

Penichet

et al. 2022

Multimed Tools Appl

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Multimodal emotion detection has been one of the main lines of research in the field of Affective Computing (AC) in recent years. Multimodal detectors aggregate information coming from different channels or modalities to determine what emotion users are expressing with a higher degree of accuracy. However, despite the benefits offered by this kind of detectors, their presence in real implementations is still scarce for various reasons. In this paper, we propose a technology-agnostic framework, HERA, to facilitate the creation of multimodal emotion detectors, offering a tool characterized by its modularity and the interface-based programming approach adopted in its development. HERA (Heterogeneous Emotional Results Aggregator) offers an architecture to integrate different emotion detection services and aggregate their heterogeneous results to produce a final result using a common format. This proposal constitutes a step forward in the development of multimodal detectors, providing an architecture to manage different detectors and fuse the results produced by them in a sensible way. We assessed the validity of the proposal by testing the system with several developers with no previous knowledge about affective technology and emotion detection. The assessment was performed applying the Computer System Usability Questionnaire and the Twelve Cognitive Dimensions Questionnaire, used by The Visual Studio Usability group at Microsoft, obtaining positive results and important feedback for future versions of the system.

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Section: Research Challenges Of Multimodal Emotion Recognition Systemsmentioning

confidence: 99%

“…As we mentioned above, the concept of multimodality has been around for quite a long time now and not just in theory. Multimodal systems have been deployed and tested in fields such as education [1,13,20,27,29,32,68], healthcare [6,7], marketing [5,30,38,42,58] or gaming [22,59].…”

Section: Multimodal Emotion Recognition Systemsmentioning

confidence: 99%

Building a three-level multimodal emotion recognition framework

García-García¹,

Lozano

Penichet

et al. 2022

Multimed Tools Appl

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“…Sekhavat et al [19] introduced Affective User Interface Design and Evaluation (AFFUIDE) to evaluate the effect of using facial expressions emotions as a user interface (UI) and system input in virtual scenarios. e data suggests that the traditional usable user interface is the most useful and that the full affective user interface feels the best fun and user experience.…”

Section: Related Work and Features Of This Research Articlementioning

confidence: 99%

“…As shown in Figure 6 and equation (19) representing the delay time calculation, B(τ) denotes the EMG signal delay time and k(Y m ), k(Y m+τ ), and k(Y m , Y m+τ ) are likelihood values.…”

Section: Scientific Programmingmentioning

confidence: 99%

“…In this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to address gesture recognition and human target identification problems. e electromyographic (EMG) signal can represent the muscles' active conditions; the data of the neural activities can be determined [19]. e advantage of electromyographic is noninvasive; thus, it executes well in studying neurological rehabilitation, motion detection, and artificial control.…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Janarthanan¹,

Venkatesh²,

Joshi

et al. 2021

Scientific Programming

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Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

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Nine Challenges for Immersive Entertainment

Lages

2023

Communications in Computer and Information Science

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Affective interaction: Using emotions as a user interface in games

Cited by 15 publications

References 65 publications

Building a three-level multimodal emotion recognition framework

Building a three-level multimodal emotion recognition framework

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Nine Challenges for Immersive Entertainment

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