IoT-Driven Facial Expression Recognition for Personalized Healthcare in Industry 5.0

Facial emotion recognition (FER) plays a critical role in understanding human behavior, especially for individuals suffering from neurological disorders (NDs) like Parkinson’s disease (PD), Multiple Sclerosis (MS), and Stroke. Early and accurate detection of emotions is crucial for both the diagnosis of associated mood disorders and continuous monitoring. However, traditional methods often fall short in providing noninvasive, realtime solutions and lack the clinical expertise necessary to identify the specific emotion types associated with each ND category. In response, this research conducted under the ALAMEDA consortium presents an Internet of Things-based FER AI Toolkit designed to enhance early diagnosis and treatment for brain diseases. The toolkit is in line with the consortium’s clinical guidelines and provides a personalized, patient-focused solution that supports the goals of Industry 5.0 in healthcare. In line with Industry 5.0 principles, the FER AI Toolkit uses edge devices to collect real-time facial data while deep learning models running on cloud servers process this data. The recognized emotions are uploaded to the Semantic Knowledge Graph (SemKG) server. This allows healthcare professionals to make informed decisions based on real-time data. Additionally, the toolkit integrates seamlessly with key components of the ALAMEDA, including the Identity Authentication Manager (IAM) for secure access and the ALAMEDA Innovation Hub (AIH) for efficient resource management. By offering continuous and personalized healthcare insights, the FER AI Toolkit helps bridge the gap between diagnosis and patient well-being, ultimately advancing healthcare systems.