Personalized Clustering for Emotion Recognition Improvement
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8110 |
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| author | Laura Gutiérrez-Martín Celia López-Ongil Jose M. Lanza-Gutiérrez Jose A. Miranda Calero |
| author_facet | Laura Gutiérrez-Martín Celia López-Ongil Jose M. Lanza-Gutiérrez Jose A. Miranda Calero |
| author_sort | Laura Gutiérrez-Martín |
| collection | DOAJ |
| description | Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements. Emotion detection should be done with fast, discrete, and non-luxurious systems working in real time and real life (wearable devices, wireless communications, battery-powered). Furthermore, emotional reactions to violence are not equal in all people. Then, large general models cannot be applied to a multi-user system for people protection, and health and social workers and law enforcement agents would welcome customized and lightweight AI models. These semi-personalized models will be applicable to clusters of subjects sharing similarities in their emotional reactions to external stimuli. This customization requires several steps: creating clusters of subjects with similar behaviors, creating AI models for every cluster, continually updating these models with new data, and enrolling new subjects in clusters when required. An initial approach for clustering labeled data compiled (physiological data, together with emotional labels) is presented in this work, as well as the method to ensure the enrollment of new users with unlabeled data once the AI models are generated. The idea is that this complete methodology can be exportable to any other expert systems where unlabeled data are added during in-field operation and different profiles exist in terms of data. Experimental results demonstrate an improvement of 5% in accuracy and 4% in F1 score with respect to our baseline general model, along with a 32% to 58% reduction in variability, respectively. |
| format | Article |
| id | doaj-art-8cc2893494fd4c2db7873fb3262d64c0 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8cc2893494fd4c2db7873fb3262d64c02025-08-20T02:01:23ZengMDPI AGSensors1424-82202024-12-012424811010.3390/s24248110Personalized Clustering for Emotion Recognition ImprovementLaura Gutiérrez-Martín0Celia López-Ongil1Jose M. Lanza-Gutiérrez2Jose A. Miranda Calero3Departamento de Tecnología Electrónica, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, SpainDepartamento de Tecnología Electrónica, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, SpainDepartamento de Ciencias de la Computación, Escuela Politécnica Superior, Universidad de Alcalá, 28805 Alcalá de Henares, SpainInstituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, SpainEmotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements. Emotion detection should be done with fast, discrete, and non-luxurious systems working in real time and real life (wearable devices, wireless communications, battery-powered). Furthermore, emotional reactions to violence are not equal in all people. Then, large general models cannot be applied to a multi-user system for people protection, and health and social workers and law enforcement agents would welcome customized and lightweight AI models. These semi-personalized models will be applicable to clusters of subjects sharing similarities in their emotional reactions to external stimuli. This customization requires several steps: creating clusters of subjects with similar behaviors, creating AI models for every cluster, continually updating these models with new data, and enrolling new subjects in clusters when required. An initial approach for clustering labeled data compiled (physiological data, together with emotional labels) is presented in this work, as well as the method to ensure the enrollment of new users with unlabeled data once the AI models are generated. The idea is that this complete methodology can be exportable to any other expert systems where unlabeled data are added during in-field operation and different profiles exist in terms of data. Experimental results demonstrate an improvement of 5% in accuracy and 4% in F1 score with respect to our baseline general model, along with a 32% to 58% reduction in variability, respectively.https://www.mdpi.com/1424-8220/24/24/8110clusteringsemi-personalized AIuser typologyunlabeled dataexportable methodologyaffective computing |
| spellingShingle | Laura Gutiérrez-Martín Celia López-Ongil Jose M. Lanza-Gutiérrez Jose A. Miranda Calero Personalized Clustering for Emotion Recognition Improvement Sensors clustering semi-personalized AI user typology unlabeled data exportable methodology affective computing |
| title | Personalized Clustering for Emotion Recognition Improvement |
| title_full | Personalized Clustering for Emotion Recognition Improvement |
| title_fullStr | Personalized Clustering for Emotion Recognition Improvement |
| title_full_unstemmed | Personalized Clustering for Emotion Recognition Improvement |
| title_short | Personalized Clustering for Emotion Recognition Improvement |
| title_sort | personalized clustering for emotion recognition improvement |
| topic | clustering semi-personalized AI user typology unlabeled data exportable methodology affective computing |
| url | https://www.mdpi.com/1424-8220/24/24/8110 |
| work_keys_str_mv | AT lauragutierrezmartin personalizedclusteringforemotionrecognitionimprovement AT celialopezongil personalizedclusteringforemotionrecognitionimprovement AT josemlanzagutierrez personalizedclusteringforemotionrecognitionimprovement AT joseamirandacalero personalizedclusteringforemotionrecognitionimprovement |