UC3M4Safety Database - List of Audiovisual Stimuli (Video)

Ruiz, María Ángeles Blanco; Martín, Laura Gutiérrez; Miranda, José Ángel Portal; Benito, Manuel Felipe Canabal; Perales, Elena Romero; Andújar, Clara Sainz de Baranda; Manuel, Rosa San Segundo; López, David Larrabeiti; Moreno, Carlos; López-Ongil, C.

doi:10.21950/luo1iz

Cited by 3 publications

(8 citation statements)

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“…Second, the emotion labels obtained during the study guaranteed the elicitation of the target emotions. The video clips selected were those with the best scores in terms of unanimity, in discrete and continuous emotions classifications, from the UC3M4Safety database [ 62 ]. The video clips’ durations were between 60 s and 119 s. The 21 volunteers labelled the emotion felt during the video clip visualization in a very close way to the target emotion, especially for video clips V04 (fear) and V01 (calm), while for the other clips, at least the PA quadrant is maintained, ( Figure 8 ).…”

Section: Discussionmentioning

confidence: 99%

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Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Gutiérrez-Martín

Romero

Andújar

et al. 2022

Sensors

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Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wearable devices are being developed for health or wellness tracking during daily life or sports activity. Likewise, other applications are being proposed for the early detection of risk situations involving sexual or violent aggressions, with the identification of panic or fear emotions. The use of other sources of information, such as video or audio signals will make multimodal affective computing a more powerful tool for emotion classification, improving the detection capability. There are other biological elements that have not been explored yet and that could provide additional information to better disentangle negative emotions, such as fear or panic. Catecholamines are hormones produced by the adrenal glands, two small glands located above the kidneys. These hormones are released in the body in response to physical or emotional stress. The main catecholamines, namely adrenaline, noradrenaline and dopamine have been analysed, as well as four physiological variables: skin temperature, electrodermal activity, blood volume pulse (to calculate heart rate activity. i.e., beats per minute) and respiration rate. This work presents a comparison of the results provided by the analysis of physiological signals in reference to catecholamine, from an experimental task with 21 female volunteers receiving audiovisual stimuli through an immersive environment in virtual reality. Artificial intelligence algorithms for fear classification with physiological variables and plasma catecholamine concentration levels have been proposed and tested. The best results have been obtained with the features extracted from the physiological variables. Adding catecholamine’s maximum variation during the five minutes after the video clip visualization, as well as adding the five measurements (1-min interval) of these levels, are not providing better performance in the classifiers.

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Section: Discussionmentioning

confidence: 99%

“…The video clips used for the experiment were selected from the UC3M4Safety Database of audiovisual stimuli aimed to elicit different emotional reactions through an immersive virtual reality environment [ 62 ] (see Figure 6 ). Most of the clips were 360-degree scenes providing more realistic experiences.…”

Section: Methodsmentioning

confidence: 99%

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Gutiérrez-Martín

Romero

Andújar

et al. 2022

Sensors

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“…The first two are the audio-visual stimuli datasets, composed of videos clips [44] and Emotional Ratings [45], respectively. They comprise a list of 79 audio-visual stimuli for emotion elicitation, which were labelled and selected among 160 clips with: 1) quality criteria, 2) balancing over different emotions, and 3) agreement among viewers [20].…”

Section: Protocol Design For the Dataset Generationmentioning

confidence: 99%

“…Database [46] Audio-visual Stimuli: Videos [ As stated in Section I, the participants for the first WEMAC experiment are women volunteers that never suffered from GBV. They belong to different age groups defined by the research team: G1 (18-24), G2 (25)(26)(27)(28)(29)(30)(31)(32)(33)(34), G3 (35)(36)(37)(38)(39)(40)(41)(42)(43)(44), G4 (45)(46)(47)(48)(49)(50)(51)(52)(53)(54), and G5 (55 on-wards). In this first release presented and detailed in this paper, we provide the data from 47 volunteers from a total of 104.…”

Section: Uc3m4safetymentioning

confidence: 99%

“…Thus, in this first release, the videos in the first and second batches are visualized and annotated in the same order by 32 and 15 people volunteers, respectively. The 28 audio-visual stimuli pool were extracted from the 79 audio-visual stimuli pool in [44], mentioned above. The 28 audio-visual stimuli were selected based on three main premises: the highest emotional discrete labelling agreement observed in women during the pre-labelling experiment [20], targeting for an adequate laboratory experiment duration, and a balanced distribution of fear vs. no-fear categories and within the four quadrants in the arousal-valence space.…”

Section: B Audio-visual Stimuli Visualizationmentioning

confidence: 99%

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WEMAC: Women and Emotion Multi-modal Affective Computing dataset

Miranda¹,

Rituerto-González²,

Gutiérrez-Martín³

et al. 2022

Preprint

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Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the Fifth SDG is a call for action to turn Gender Equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Within this context, the UC3M4Safety research team aims to develop Bindi. This is a cyber-physical system which includes embedded Artificial Intelligence algorithms, for user real-time monitoring towards the detection of affective states, with the ultimate goal of achieving the early detection of risk situations for women. On this basis, we make use of wearable affective computing including smart sensors, data encryption for secure and accurate collection of presumed crime evidence, as well as the remote connection to protecting agents. Towards the development of such system, the recordings of different laboratory and into-the-wild datasets are in process. These are contained within the UC3M4Safety Database. Thus, this paper presents and details the first release of WEMAC, a novel multi-modal dataset, which comprises a laboratory-based experiment for 47 women volunteers that were exposed to validated audio-visual stimuli to induce real emotions by using a virtual reality headset while physiological, speech signals and self-reports were acquired and collected. We believe this dataset will serve and assist research on multi-modal affective computing using physiological and speech information.

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Bindi: Affective Internet of Things to Combat Gender-Based Violence

Miranda

Rituerto-González

Luis-Mingueza

et al. 2022

IEEE Internet Things J.

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The main research motivation of this paper is the fight against gender-based violence and achieving gender equality from a technological perspective. The solution proposed in this work goes beyond currently existing panic buttons, needing to be manually operated by the victims under difficult circumstances. Instead, Bindi, our end-to-end autonomous multimodal system, relies on artificial intelligence methods to automatically identify violent situations, based on detecting fear-related emotions, and trigger a protection protocol, if necessary. To this end, Bindi integrates modern state-of-the-art technologies, such as the Internet of Bodies, affective computing, and cyber-physical systems, leveraging i) affective IoT with auditory and physiological commercial off-the-shelf smart sensors embedded in wearable devices, ii) hierarchical multisensorial information fusion, and iii) the edge-fog-cloud IoT architecture. This solution is evaluated using our own dataset named WEMAC, a very recently collected and freely-available collection of data comprising the auditory and physiological responses of 47 women to several emotions elicited by using a virtual reality environment. On this basis, this work provides an analysis of multimodal late fusion strategies to combine the physiological and speech data processing pipelines to identify the best intelligence engine strategy for Bindi. In particular, the best data fusion strategy reports an overall fear classification accuracy of 63.61% for a subject-independent approach. Both a power consumption study and an audio data processing pipeline to detect violent acoustic events complement this analysis. This research is intended as an initial multimodal baseline that facilitates further work with real-life elicited fear in women.

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UC3M4Safety Database - List of Audiovisual Stimuli (Video)

Cited by 3 publications

References 0 publications

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration

WEMAC: Women and Emotion Multi-modal Affective Computing dataset

Bindi: Affective Internet of Things to Combat Gender-Based Violence

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