LONG-REMI: An AI-Based Technological Application to Promote Healthy Mental Longevity Grounded in Reminiscence Therapy

Àngela Nebot, Sara Domènech, Natália Albino-Pires, Francisco Mugica, Anass Benali, Xènia Porta, Oriol Nebot and Pedro M. Santos
Additional contact information
Àngela Nebot: Soft Computing Research Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politènica de Catalunya, 08034 Barcelona, Spain
Sara Domènech: Fundació Salut i Envelliment, Universitat Autònoma de Barcelona, 08041 Barcelona, Spain
Natália Albino-Pires: Escola Superior de Educação, Instituto Politécnico de Coimbra, 3030-329 Coimbra, Portugal
Francisco Mugica: Soft Computing Research Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politènica de Catalunya, 08034 Barcelona, Spain
Anass Benali: Soft Computing Research Group at Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politènica de Catalunya, 08034 Barcelona, Spain
Xènia Porta: Fundació Salut i Envelliment, Universitat Autònoma de Barcelona, 08041 Barcelona, Spain
Oriol Nebot: UX/UI Dessign Department, Universitat Oberta de Catalunya Barcelona, 08035 Barcelona, Spain
Pedro M. Santos: CINTESIS—Center for Health Technology and Services Research, Universidad de Lusófona Humanidades e Tecnologias, 1749-024 Lisboa, Portugal

IJERPH, 2022, vol. 19, issue 10, 1-15

Abstract: Reminiscence therapy (RT) consists of thinking about one’s own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering RT involves the use of technology-assisted applications, which must also satisfy the needs of the user. This study aimed to develop an AI-based computer application that recreates RT in a personalized way, meeting the characteristics of RT guided by a therapist or a caregiver. The material guiding RT focuses on intangible cultural heritage. The application incorporates facial expression analysis and reinforcement learning techniques, with the aim of identifying the user’s emotions and, with them, guiding the computer system that emulates RT dynamically and in real time. A pilot study was carried out at five senior centers in Barcelona and Portugal. The results obtained are very positive, showing high user satisfaction. Moreover, the results indicate that the high frequency of positive emotions increased in the participants at the end of the intervention, while the low frequencies of negative emotions were maintained at the end of the intervention.

Keywords: reminiscence therapy; cognitive impairment; intangible cultural heritage; emotions recognition; face tracking; reinforcement learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/10/5997/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/10/5997/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:10:p:5997-:d:815951

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().