INTELLIGENT RECOGNITION OF MULTIMODAL HUMAN ACTIVITIES FOR PERSONAL HEALTHCARE

Abstract

The rapid growth of wearable technologies, smart sensors, and artificial intelligence has significantly advanced the development of intelligent healthcare systems. This paper presents an innovative approach to multimodal Human Activity Recognition (HAR) aimed at enhancing personal healthcare monitoring. Unlike traditional systems that rely on single-modal data and suffer from limitations such as noise sensitivity and reduced accuracy, the proposed system integrates multiple data sources including accelerometers, gyroscopes, cameras, and physiological sensors to achieve more reliable and accurate activity recognition. The system employs advanced machine learning and deep learning techniques to analyze sensor data and classify daily human activities such as walking, sitting, sleeping, and exercising. By leveraging multimodal data fusion, the system improves robustness and minimizes the impact of missing or inconsistent data. This approach is particularly beneficial for healthcare applications such as elderly care, rehabilitation monitoring, chronic disease management, and early detection of abnormal activities like falls. Additionally, the system provides real-time monitoring and alert generation, enabling timely medical intervention and better decision-making for both users and healthcare professionals. Despite its advantages, challenges such as data fusion complexity, privacy concerns, computational requirements, and scalability are also addressed. Experimental results demonstrate that the proposed multimodal system outperforms traditional singlemodal approaches in terms of accuracy and reliability. In conclusion, multimodal HAR systems offer a promising solution for continuous, context-aware, and personalized healthcare monitoring. Future enhancements may include the integration of edge computing and privacy-preserving techniques to further improve system efficiency and user trust in real-world applications. Keywords Multimodal Human Activity Recognition (HAR), Personal Healthcare, Wearable Sensors, Machine Learning, Deep Learning, Data Fusion, Smart Healthcare Systems, Activity Monitoring, Internet of Things (IoT), Real-Time Health Monitoring, Artificial Intelligence, Healthcare Analytics.