[Reminiscence program supported by artificial intelligence and based on intangible cultural heritage aimed at the elderly: A pilot study].

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.

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.

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.

Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inductive reasoning, genetic fuzzy systems, neural networks, and nonlinear autoregressive techniques; the results obtained so far have been significant, but not solid enough to describe the control response of the CNS over the CS. In this research, support vector machines (SVMs) are used to predict the response of a branch of the CNS, specifically, the one that controls an important part of the cardiovascular system. To do this, five models are developed to emulate the output response of five controllers for the same input signal, the carotid sinus blood pressure (CSBP).

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.