Human Activity Recognition in Enhancing Healthcare for Aging Populations: Challenges, Innovations, and Future Directions
Mohamed Abderrahmen Boulahia, Saber Benharzallah, Faiza Titouna, Tahar Dilekh · 2024
As the global population ages, healthcare and social systems face escalating demands. Effective solutions for promoting healthy aging increasingly rely on deep learning and Internet of Things technologies to provide personalized, preventive, and proactive care. Central to these efforts is human activity recognition, which enables the continuous monitoring and assessment of daily activities—a vital indicator of well-being and independence in older adults. This paper highlights key concepts and methodologies in applying deep learning and internet of things in human activity recognition, emphasizing its role in understanding and enhancing the quality of life for the elderly through accurate, activity-based health insights. However, most current studies continue to emphasize achieving high accuracy in machine learning models, despite already reaching substantial levels of precision. This paper argues for the need to shift focus towards developing adaptable models capable of recognizing new and varied activities. Additionally, it highlights the importance of recommendation systems in healthcare, which should extend beyond mere activity recognition to provide actionable advice and preventive measures for the elderly. This study also addresses the gap in the literature by providing a comprehensive review of existing approaches and identifying key research directions that should be prioritized in the field. By signaling the lack of comprehensive studies offering in-depth insights like this paper, it underscores the necessity of moving beyond current achievements towards more dynamic and context-aware solutions in human activity recognition technologies.