Film mood induction and emotion classification using physiological signals for health and wellness promotion in older adults living alone

This paper introduces a wearable hardware/software system specifically tailored to detect seven emotions (neutral, tenderness, amusement, anger, disgust, fear, and sadness) aimed at promoting health and wellness in older adults living alone at home. The complete software and hardware architectures a...

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Detalles Bibliográficos
Autores: Martínez Rodrigo, Arturo, Fernández Aguilar, María de la Luz, Zangroniz Cantabrana, Roberto, Latorre Postigo, José Miguel, Pastor García, Jose Manuel, Fernández Caballero, Antonio
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/35126
Acceso en línea:https://doi.org/10.1111/exsy.12425
https://hdl.handle.net/10578/35126
Access Level:acceso abierto
Palabra clave:Emotion classification
Health promotion
Mood induction
Older adult
Physiological signals
Wearable
Wellness promotion
Descripción
Sumario:This paper introduces a wearable hardware/software system specifically tailored to detect seven emotions (neutral, tenderness, amusement, anger, disgust, fear, and sadness) aimed at promoting health and wellness in older adults living alone at home. The complete software and hardware architectures acquiring and processing electrodermal activity and photoplethysmography signals are introduced. The wearable emotion detection system is trained by eliciting the desired emotions on 39 older adults through a film mood induction procedure. Seventeen features are calculated on skin conductance response and heart rate variability data, grouped into five statistical, four temporal, and eight morphological features. Then, these features are used to run emotion classification considering support vector machines, decision trees, and quadratic discriminant analysis. In line with psychological findings, the results offer a global accuracy of 82% in negative emotion (anger, disgust, fear, and sadness) classification. For positive emotions (tenderness and amusement), also in conformity with previous psychological outcomes, amusement shows the highest ratio of hits (92%) but tenderness the lowest one (66%). These results demonstrate that our wearable emotion detection system can be used by ageing adults, especially for detecting negative emotions that usually damage health and wellness and lead to social isolation.