A Framework for Recognizing and Regulating Emotions in the Elderly

Castillo, José Carlos; Fernández-Caballero, Antonio; Castro‐González, Álvaro; Salichs, Miguel Á.; López, María Teresa

doi:10.1007/978-3-319-13105-4_46

Cited by 42 publications

(17 citation statements)

References 17 publications

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“…This article is based on the assumption of the power of color to change mood. The described proposal falls within the project"Improvement of the Elderly Quality of Life and Care through Smart Emotion Regulation" [2], [3]. The objective of the project is to find solutions for improving the quality of life and care of the elderly who can or wants to continue living at home by using emotion detection and regulation means.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Color Preference for Emotion Regulation

Sokolova

Fernández-Caballero

Ros

et al. 2015

Lecture Notes in Computer Science

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Abstract. This paper introduces a study on the relationship between emotion regulation and color preference. In the described pilot study, participants are asked to label uniform color images by using opposite meaningful words belonging to four semantic scales, namely "Tension" (ranging from Relax to Stress), "Temperature" (Coldness to Warmness), "Amusement" (Boredom to Fun) and "Attractiveness" (Pleasantness to Unpleasantness). Simultaneously, the participants have to indicate if they feel certain emotions while observing each colored image, as well as to rate the intensity of the feeling. The labeled emotions are "Joy", "Happiness", and "Sadness". The results demonstrate that people generally perceive color emotions for one-colored images in similar ways, though showing some variations for males and females. Several conclusions about the relations between color and emotions are presented.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Color Preference for Emotion Regulation

Sokolova

Fernández-Caballero

Ros

et al. 2015

Lecture Notes in Computer Science

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“…New architectures and methodologies are providing significant advances in this research area (Castillo, Fernández-Caballero, Castro-Gonzalez, Salichs, & López, 2014;Fernández-Caballero et al, 2016).…”

Section: Of 14mentioning

confidence: 99%

“…However, the affective computing paradigm enables to decide an adequate response to human emotional stimuli (Peralta, Fernández‐Caballero, Latorre, Olivas, & Conde, ; Poria, Cambria, Bajpai, & Hussain, ; Sokolova & Fernández‐Caballero, ). New architectures and methodologies are providing significant advances in this research area (Castillo, Fernández‐Caballero, Castro‐Gonzalez, Salichs, & López, ; Fernández‐Caballero et al, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…The power of calculation and the large storage capacity of ML and BD provides a way of solving this issue [15]. For this reason, it is necessary to create tools that allow the adaptation, integration and analysis of all physiological data obtained [7,16]. Therefore, the main objective of this study is to establish the foundations of a distributed system based on non-relational databases (NoSQL) for the acquisition, processing and visualisation of physiological signals.…”

Section: Introductionmentioning

confidence: 99%

Distributed Architecture for Acquisition and Processing of Physiological Signals

Sánchez-Reolid

García

Vicente-Querol

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The increase in the number of devices equipped with physiological sensors and their low price mean that they can be used in many fields. One of these fields is health-care and home-care for the elderly or people with disabilities. The development of such devices makes it possible to monitor their condition continuously and at all times. A continuous monitoring not only establishes an image of the user’s status, but also detects possible anomalies. Therefore, it is necessary to develop a distributed architecture that allows expert analysts to access the data provided by the sensors at all times and from anywhere. This paper introduces the development and implementation of the concept of distributed architecture, focusing on the minimum requirements needed to carry it out. All the necessary modules are described for different stages: acquisition, communication and processing of physiological signals. The last stage is carried out by a machine learning system. The complete reporting and storage system is also described. Finally, the most important conclusions that have emerged during the development are reported.

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A Framework for Recognizing and Regulating Emotions in the Elderly

Cited by 42 publications

References 17 publications

Evaluation of Color Preference for Emotion Regulation

Evaluation of Color Preference for Emotion Regulation

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

Distributed Architecture for Acquisition and Processing of Physiological Signals

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