Radar-based Human Activity Recognition (HAR) has attracted much attention in various fields such as smart security, medical monitoring, and human computer interaction. Integrating Convolutional Neural Networks (CNNs) with radar spectrum techniques for HAR is becoming increasingly popular. However, traditional network models usually have a large number of parameters and require long training and inference times, making them less suitable for real-time applications. To address these issues, this study proposes a lightweight CNN model based on Frequency-Modulated Continuous Wave (FMCW) radar, designed for edge devices for efficient real-time monitoring. We compare three different 2D domain radar data preprocessing techniques - Time Range (TR), Short-Time Fourier Transform (STFT), and Smoothed Pseudo-Wigner-Ville Distribution (SPWVD) - along with four state-of-the-art neural networks. Our approach achieves high accuracy in HAR classification and effectively addresses the challenges posed by limited radar data through Transfer Learning (TL), demonstrating the potential for real-time applications. After evaluating 12 configurations of CNN models and preprocessing methods, we found that MobileNetV2 with STFT was the most efficient and lightweight, with STFT taking only 220 ms to generate a spectrogram sample. This combination achieved an inference time of only 2.57 ms per sample and a recognition accuracy of 96.30%, setting a new benchmark for real-time intelligent systems on edge devices.