A novel lightweight hand gesture recognition approach that is based on Frequency-Modulated Continuous Wave (FMCW) RADAR, which aims to minimize computational complexity and memory usage as well as maintain a high recognition performance, is proposed in this paper. Most of the existing methods utilize two-dimensional (2D) or three-dimensional (3D) features that are combined with complex neural network structures, which result in high computational costs. The proposed approach in contrast extracts four components, which include range, Doppler, azimuth, and elevation, as the one-dimensional (1D) time-series features. These features are fed into a neural network that comprises of a multi-branch temporal convolutional network, depthwise separable convolutions, and a channel attention mechanism in order to enhance the classification performance. The experiments were conducted with 9 hand gestures that were collected from 9 participants. The proposed system achieved a high accuracy of 99.38% with only 44.6K parameters and 1.84M FLOPs. Extensive ablation studies and comparative experiments against the existing models demonstrated that the proposed method effectively balances the performance and computational efficiency. This study validates the expressive capability of 1D features for hand gesture recognition and suggests practical applicability in resource-constrained environments, such as embedded systems.