The growing trend of solitary living among the elderly and young, coupled with the high risk of falls leading to injuries and death, highlights the need for fall monitoring systems. Emphasizing individuals' privacy and comfort, these systems should rely on radar sensors instead of visual-based, acoustic-based, or wearable solutions. Current radar-based systems are yet to reach satisfactory real-world performance. This work proposes a radar-based fall detection system with superior performance in complex real-world scenarios while maintaining edge computing capabilities and minimum hardware resources. The proposed deep learning system achieved a recall of 98.99% and a precision of 99.32%. These unprecedented performance numbers are measured on the proposed dataset, which is the most real-life representative dataset in the literature. The system has 211.8k parameters and ~8.84 M Floating Point Operations (FLOPs), achieving an edge computing capability. Moreover, the efficient model construction eliminates redundant computation in real-time operation. Furthermore, this work proposes a novel metric that encompasses all dataset's quality aspects into a single number, which can be applied to all classification problems. This metric can then be used as a correction factor for performance metrics to put them in the context of the dataset used for testing.