DESIGNING A MULTIMODAL MODEL FOR HUMAN FALL DETECTION IN HUMAN ACTIVITY RECOGNITION USING VIDEO AND SENSORS

Abstract

Human activity recognition is a crucial area of research in the healthcare industry. In recent years, the aging of the global population has emerged as a pressing concern. The incidence of falls is a leading cause of mortality among the elderly in many countries, making fall detection an essential component of smart home systems. There are two primary methods of detecting falls: wearable sensor-based fall detection and vision-based fall detection. The former employs sensor devices attached to individuals, while the latter relies on video cameras to monitor the environment and detect falls via video processing. In the field of fall detection, the vision-based method is deemed superior due to its cost-effectiveness and convenience relative to other methods, such as wearable sensors. This thesis aims to present a proposal for a multimodal (hybrid) system for human fall detection. The proposed system draws from various deep learning approaches applicable to human fall detection.