Novel Approaches to Personalized Human ActivityRecognition in Elderly Care via Machine and Deep Learning

This research seminar explores innovative approaches to <b>personalized human activity recognition (HAR)</b> systems in the context of <b>elderly care</b>, leveraging both <b>machine learning</b> and <b>deep learning</b> techniques. The study focuses on designing intelligent models capable of accurately identifying and classifying daily human activities using data from sensors and wearable devices. By incorporating personalized learning algorithms and adaptive neural architectures, the proposed system enhances recognition accuracy for individual users, addressing the variability in movement patterns among elderly individuals. The outcomes of this research contribute toward improving assistive technologies, promoting independent living, and ensuring timely intervention in healthcare monitoring systems.