Digital Technologies for Innovative Mental Health Rehabilitation

TLDR

Schizophrenia is a chronic mental illness that disrupts reality perception, impairs daily functioning, and is traditionally treated with antipsychotics, while other symptoms require multidisciplinary approaches such as cognitive training and therapy, and immersive technologies like serious games and virtual reality are emerging to enhance recovery and quality of life. This study proposes digital technologies to create innovative mental‑health rehabilitation tools and plans a clinical trial to evaluate their effectiveness on negative symptoms in schizophrenia patients. The system employs machine‑learning algorithms to analyze biometric and physiological signals in real time, dynamically adjusting exercise difficulty to match patients’ stress levels and performance. Currently under development in partnership with a health unit, the integrated hardware‑software platform aims to provide a rehabilitation tool for patients with schizophrenia.

Abstract

Schizophrenia is a chronic mental illness, characterized by the loss of the notion of reality, failing to distinguish it from the imaginary. It affects the patient in life’s major areas, such as work, interpersonal relationships, or self-care, and the usual treatment is performed with the help of anti-psychotic medication, which targets primarily the hallucinations, delirium, etc. Other symptoms, such as the decreased emotional expression or avolition, require a multidisciplinary approach, including psychopharmacology, cognitive training, and many forms of therapy. In this context, this paper addresses the use of digital technologies to design and develop innovative rehabilitation techniques, particularly focusing on mental health rehabilitation, and contributing for the promotion of well-being and health from a holistic perspective. In this context, serious games and virtual reality allows for creation of immersive environments that contribute to a more effective and lasting recovery, with improvements in terms of quality of life. The use of machine learning techniques will allow the real-time analysis of the data collected during the execution of the rehabilitation procedures, as well as enable their dynamic and automatic adaptation according to the profile and performance of the patients, by increasing or reducing the exercises’ difficulty. It relies on the acquisition of biometric and physiological signals, such as voice, heart rate, and game performance, to estimate the stress level, thus adapting the difficulty of the experience to the skills of the patient. The system described in this paper is currently in development, in collaboration with a health unit, and is an engineering effort that combines hardware and software to develop a rehabilitation tool for schizophrenic patients. A clinical trial is also planned for assessing the effectiveness of the system among negative symptoms in schizophrenia patients.