## 1 Introduction  
The paper presents a complex health monitoring platform, NeuroPredict, focused on managing neurodegenerative diseases by monitoring both physical and mental health and the emotion recognition algorithm integrated into the platform. NeuroPredict has been designed and is currently under development in our laboratory as part of a four-year research project, currently in its second year of implementation. The NeuroPredict platform aims to monitor and manage patients suffering from one of the following three neurodegenerative conditions: Alzheimer's disease (AD), Parkinson's disease (PD), or multiple sclerosis (MS). In the context of integrating IoMT (Internet of Medical Things) and AI (artificial intelligence) for proactive healthcare, our focus is on predictive models for managing the three targeted neurodegenerative diseases. Emotion detection from voice is integrated into the presented platform to highlight the connection between mental health and physical health and as a starting point for further analysis of the voice signals, enabling the development of predictive models tailored to the targeted diseases based on speech features. It is a known fact that the world population is aging, and this is also shown in the latest Ageing Report of the European Commission [1], a report that aims to analyze the aging of the population within the European Union and the financial impact of the demographic changes on the economies of each state. The 2024 Ageing Report predicts a very clear upward shift in the age distribution across all member states of the European Union (EU), with a consistent and continuous growth of the old age population--aged 65 and over. This shift will be reflected in the old-age dependency ratio (the old-age population divided by the working population--aged 20 to 64), leading to an increased need for caregivers for the elderly who cannot tend for themselves. Among the age-related problems, cognitive decline is one of the most common. For AD and PD, old age is the most significant risk factor [2; 3]. There are other neurodegenerative diseases, such as MS, that have become more prevalent in recent years but are not necessarily age-related. Key risk factors and reasons for the increase in prevalence of neurodegenerative diseases are related to lifestyle factors like poor dietary habits, physical inactivity, smoking and alcohol use, certain medications, and pre-existing medical conditions like cardiovascular diseases and chronic inflammation [4]. By continuously monitoring these factors over extended periods, the importance of each of these factors will be better understood, and in the long run, the targeted medical conditions will be better and more easily diagnosed. **The aim of this paper** is to present the NeuroPredict platform and the development of an emotion detection algorithm based on voice features specifically aimed at patients with neurodegenerative diseases, integrated into the NeuroPredict platform. Voice has been chosen as an easy-to-acquire signal that reflects information about the emotional state and may also be analyzed for neurological disorder recognition. The work emphasizes the use of advanced AI techniques for the monitoring and management of PD, AD, and MS. The paper explores how the NeuroPredict platform integrates IoMT devices with AI-driven data collection and real-time analysis to enable early detection and support personalized long-term care. By examining the platform's architecture and predictive modeling capabilities, the paper aims to show how AI enhances proactive healthcare, improving patient outcomes and quality of life amidst the challenges of an aging population. This research contributes to the field of emotional state monitoring, particularly in the realm of neurodegenerative diseases, by designing and integrating an effective voice-based emotion recognition algorithm into the NeuroPredict platform.