Fall detection and recognition play a crucial role in enabling timely medical interventions for people who are at risk of falls, especially among vulnerable populations like older adults and those with mobility limitations. In this paper, a cost-effective integrated sensing and communication system, namely FallDR, is presented for fall detection and recognition using ultra-wideband communication. Firstly, we collected the time of flight information of falls (four types) and non-fall events by 10 participants using FallDR. We then proposed a convolutional neural network incorporated with squeeze-and-excitation blocks to detect and recognize falls based on fall trajectories. It proves that the proposed model is accurate, energy-efficient, and lightweight to achieve 100% accuracy in fall detection and recognition. Our proposed solution is proven to be highly robust against environmental changes such as interference, distance, and direction changes. Further tests in an office showed that FallDR could achieve nearly 100% accuracy, even when the environment was changed. FallDR efficiently employs the characteristics of fall trajectory and the advanced modeling ability of the neural network. We have published our archived datasets and code for comparisons and improvements.