MAGIC: Meta-Learning Adaptive Gesture Recognition with mmWave MIMO CSI
Khandaker Foysal Haque, K M Rumman, Arman Elyasi, Francesca Meneghello, Francesco Restuccia · 2025
In this paper, we present MAGIC, a novel approach to gesture recognition utilizing mmWave multiple-input multiple-output (MIMO) Channel State Information (CSI). Unlike existing mmWave gesture recognition methods that often rely on radar signals, MAGIC leverages CSI extracted from mmWave MIMO integrated sensing and communication (ISAC) systems. While advanced radar systems, such as those operating in frequency-modulated continuous wave (FMCW) mode, can achieve high frequency and spatial resolution, they typically require dedicated sensing infrastructure, which increases system complexity. In contrast, MAGIC utilizes high-granular CSI from orthogonal frequency-division multiplexing (OFDM) systems, enabling fine spatial, temporal, and frequency-domain information for robust gesture recognition. This eliminates the need for dedicated radar transceivers, simplifying the system and reducing transmission overhead. MAGIC employs a learning-based architecture, integrating a temporal convolutional network (TCN) to classify gestures by capturing long-range temporal dependencies. To address the critical challenge of domain adaptation in gesture recognition, we propose adaptive temporal embedding network (ATEN), a meta-learning framework that combines the temporal modeling capabilities of TCN with task-specific adaptation mechanisms. We evaluateMAGIC through a comprehensive data collection campaign involving two subjects performing 10 micro gestures across three different environments, with synchronized video streams providing the ground truth. The proposed system achieves a baseline accuracy of 99.24% using TCN. The system continues to perform well – achieving up to 98.82% accuracy – when adapting to new domains using ATEN, outperforming other state-of-the-art domain adaptation methods by 14% on average.