Programa de reminiscencia apoyado en inteligencia artificial y basado en el patrimonio cultural inmaterial dirigido a personas mayores: un estudio piloto / Reminiscence program supported by artificial intelligence and based on intangible cultural heritage aimed at the elderly: A pilot study

Introducción: Las aplicaciones tecnológicas son una forma innovadora de brindar la terapia de reminiscencia (TR) y deben satisfacer las necesidades de las personas usuarias. El patrimonio cultural inmaterial (PCI) como tópico para dicha terapia no ha sido todavía explorado. Evaluamos la disponibilidad de una nueva aplicación apoyada en inteligencia artificial (IA) para la TR basada en el PCI dirigida a personas mayores. Material y métodos: Estudio observacional prospectivo realizado con personas de 65 años o más, sin deterioro cognitivo y con deterioro cognitivo leve y moderado que acudían a seis centros de mayores en España y Portugal. Las personas participantes testaron el primer prototipo del programa individualizado LONG-REMI en cuatro sesiones semanales consecutivas. La usabilidad y la satisfacción de la experiencia fueron evaluadas mediante la Escala visual analógica (EVA) al finalizar la intervención. Las emociones se evaluaron mediante la escala de afecto positivo y afecto negativo (PANAS) antes y al finalizar la intervención. Resultados: Se analizaron datos de 56 personas, la usabilidad y la satisfacción fueron altamente percibidas, con puntuaciones de 7,75±1,88 y 8,38±1,57, respectivamente. La subescala de subafecto positivo PANAS mostró cambios significativos (28,86±8,88 antes de la intervención vs. 36,70±9,43 post intervención, Z = -4,18, p = 0,000). No hubo cambios significativos en la subescala de afecto negativo PANAS. Conclusiones: El primer prototipo de la aplicación tecnológica LONG-REMI puede ser usado en personas mayores sin y con deterioro cognitivo. Este puede ser un instrumento potencial para futuras terapias cognitivas con actividades estimulantes y beneficio en las emociones. (AU)

Introduction: Technological applications are an innovative way of providing reminiscence therapy and must meet the users’ needs. Intangible cultural heritage as a basis for such therapy has not been explored yet. We evaluated the applicability of a new technological application supported by artificial intelligence for reminiscence therapy based on intangible cultural heritage aimed at older people. Material and methods: A prospective observational study was carried out with people aged 65 or over, without cognitive impairment and with mild and moderate cognitive impairment who attended six centers for older people in Spain and Portugal. Participants tested the first prototype of the individualized LONG-REMI program in four consecutive weekly sessions. The usability and satisfaction of the experience were evaluated using the VAS scale at the end of the intervention. Emotions were evaluated using the PANAS scale before and at the end of the intervention. Results: Data from 56 participants were analysed. For all participants, usability and satisfaction were highly perceived, with scores of 7.75±1.88 and 8.38±1.57, respectively. The positive affect subscale PANAS showed significant changes (28.86±8.88 before the intervention versus 36.70±9.43 post intervention, Z = −4.18, P = 0.000). There were no significant changes in the PANAS negative affect subscale. Conclusions: The first prototype of the LONG-REMI technological application can be used by older people both with and without cognitive impairment. This has the potential to be an instrument for future cognitive therapies with stimulating activities and benefits for emotions. (AU)

[Reminiscence program supported by artificial intelligence and based on intangible cultural heritage aimed at the elderly: A pilot study]. / Programa de reminiscencia apoyado en inteligencia artificial y basado en el patrimonio cultural inmaterial dirigido a personas mayores: un estudio piloto.

INTRODUCTION: Technological applications are an innovative way of providing reminiscence therapy and must meet the users' needs. Intangible cultural heritage as a basis for such therapy has not been explored yet. We evaluated the applicability of a new technological application supported by artificial intelligence for reminiscence therapy based on intangible cultural heritage aimed at older people. MATERIAL AND METHODS: A prospective observational study was carried out with people aged 65 or over, without cognitive impairment and with mild and moderate cognitive impairment who attended six centers for older people in Spain and Portugal. Participants tested the first prototype of the individualized LONG-REMI program in four consecutive weekly sessions. The usability and satisfaction of the experience were evaluated using the VAS scale at the end of the intervention. Emotions were evaluated using the PANAS scale before and at the end of the intervention. RESULTS: Data from 56 participants were analysed. For all participants, usability and satisfaction were highly perceived, with scores of 7.75±1.88 and 8.38±1.57, respectively. The positive affect subscale PANAS showed significant changes (28.86±8.88 before the intervention versus 36.70±9.43 post intervention, Z = -4.18, P = 0.000). There were no significant changes in the PANAS negative affect subscale. CONCLUSIONS: The first prototype of the LONG-REMI technological application can be used by older people both with and without cognitive impairment. This has the potential to be an instrument for future cognitive therapies with stimulating activities and benefits for emotions.

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

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.

On-The-Fly Syntheziser Programming with Fuzzy Rule Learning.

This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of source code. To facilitate this, one possibility is to automatically create variations out of a few synthesizer presets. However, the need for real-time feedback makes existent synthesizer programmers unfeasible to use. In addition, sometimes presets are created mid-performance and as such no benchmarks exist. Inductive rule learning has shown to be effective for creating real-time variations in such a scenario. However, logical IF-THEN rules do not cover the whole feature space. Here, we present an algorithm that extends IF-THEN rules to hyperrectangles, which are used as the cores of membership functions to create a map of the input space. To generalize the rules, the contradictions are solved by a maximum volume heuristics. The user controls the novelty-consistency balance with respect to the input data using the algorithm parameters. The algorithm was evaluated in live performances and by cross-validation using extrinsic-benchmarks and a dataset collected during user tests. The model's accuracy achieves state-of-the-art results. This, together with the positive criticism received from live coders that tested our methodology, suggests that this is a promising approach.

A model for continuous monitoring of patients with major depression in short and long term periods.

BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. METHOD: This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. RESULTS: A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. CONCLUSION: The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.

PM2.5 emission elemental composition.

A field study was carried out from 2003 to 2004 with the aim to develop the PM2.5 emission source profiles from light duty gasoline and heavy-duty diesel vehicles, as well as emission source profiles from waste incineration, wood burning and meatbroiling. Over 25 chemical species were quantified from the fine particles emitted by the different combustion sources investigated, including organic and elemental carbon, ions and elements. The OC/TC ratio found in the different PM2.5 profiles was dissimilar as well as the sulfate, nitrate, ammonium, soil species and trace element content. Consequently these combustion emission profiles could be used in source reconciliation studies for fine particles.A field study was carried out from 2003 to 2004 with the aim to develop the PM2.5 emission source profiles from light-duty gasoline and heavy-duty diesel vehicles, as well as emission source profiles from waste incineration, wood burning, LP gas combustion, and meat broiling. Over 25 chemical species were quantified from the fine particles emitted by the different combustion sources investigated, including organic and elemental carbon, ions, and elements. The OC/TC ratio found in the different PM2.5 profiles was dissimilar as well as the sulfate, nitrate, ammonium, soil species, and trace element content. Consequently, these combustion emission profiles could be used in source reconciliation studies for fine particles.