Lightweight Pose-Aware CNN for Real-Time Detection of Health Emergencies on Edge Devices

Lightweight pose-aware Convolutional Neural Networks (CNNs), integrated with edge computing technologies, have created new opportunities in the field of real-time health emergency detection. Such systems use visual information about human body posture and behavior to detect critical medical events such as falls or immobility and to provide immediate and localized responses without relying on cloud infrastructure. This review examines the state-of-the-art in pose-aware CNNs optimized for deployment on edge devices, emphasizing their design, optimization techniques, deployment challenges, and application use cases. It draws insights from adjacent domains, including education and animal health monitoring, and illustrates how these cross-disciplinary advancements can inform healthcare solutions. The discussion covers an in-depth examination of hardware specifications, environmental limitations, energy efficiency, data privacy, and scalability of the system. By analyzing the current architectures and future opportunities, this review shows the potential of lightweight pose-aware CNNs to transform health monitoring to be both proactive and accessible.