Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

• Published in: Algorithms Vol. 17; no. 9; p. 376
• Main Authors: Sandulescu, Virginia, Ianculescu, Marilena, Valeanu, Liudmila, Alexandru, Adriana
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Age; Aging; Akinesia; Algorithms; Alzheimer's disease; Artificial intelligence; Computational linguistics; Computer platforms; Disease; emotion detection; Emotion recognition; Emotions; Forecasts and trends; Health care; health monitoring platform; Internet of medical things; Language processing; Literature reviews; Mental health; Movement disorders; Natural language interfaces; Nervous system diseases; Patients; Population; Prediction models; predictive modeling; Respiration; Restless legs syndrome; Signal quality; Sleep disorders; voice processing; Voice recognition; United Kingdom
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17090376

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.