Investigating the Generalizability of Emotion Detection via Wearable Physiological Sensors: EmoWear Usecase

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199902" target="_blank" >RIV/00216305:26220/26:0199902 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268739" target="_blank" >http://dx.doi.org/10.1109/icumt67815.2025.11268739</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268739" target="_blank" >10.1109/icumt67815.2025.11268739</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Investigating the Generalizability of Emotion Detection via Wearable Physiological Sensors: EmoWear Usecase

Popis výsledku v původním jazyce

Emotion detection is increasingly recognized as a foundational component in the evolution of intelligent eHealth systems. This paper presents a personalized methodology for identifying relevant physiological sensors for emotion recognition, using the open-access EmoWear dataset as a case study. The proposed framework applies statistical feature extraction and correlation analysis to examine the relationship between biosignals and emotional states, specifically valence, arousal, and dominance. The findings indicate that no single sensor modality universally correlates with emotional states across individuals, reinforcing the need for personalized and multimodal approaches. Notably, Electrodermal Activity (EDA) activity showed a higher correlation with valence, whereas Skin Temperature (SKT) was more closely associated with arousal; however, inter-individual variability remained significant. Overall, the analysis highlights challenges for generalizability in affective computing and emphasizes the importance of context and individual differences in emotion expression.

Název v anglickém jazyce

Investigating the Generalizability of Emotion Detection via Wearable Physiological Sensors: EmoWear Usecase

Popis výsledku anglicky

Emotion detection is increasingly recognized as a foundational component in the evolution of intelligent eHealth systems. This paper presents a personalized methodology for identifying relevant physiological sensors for emotion recognition, using the open-access EmoWear dataset as a case study. The proposed framework applies statistical feature extraction and correlation analysis to examine the relationship between biosignals and emotional states, specifically valence, arousal, and dominance. The findings indicate that no single sensor modality universally correlates with emotional states across individuals, reinforcing the need for personalized and multimodal approaches. Notably, Electrodermal Activity (EDA) activity showed a higher correlation with valence, whereas Skin Temperature (SKT) was more closely associated with arousal; however, inter-individual variability remained significant. Overall, the analysis highlights challenges for generalizability in affective computing and emphasizes the importance of context and individual differences in emotion expression.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3315-7675-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

199-204

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Florence, Italy

Datum konání akce

3. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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