MTxLSTM: Multi-Task Learning for Gesture Recognition and Person Identification Using a Miniature Radar Sensor

Radar-based gesture recognition and person identification offer a natural, convenient, and privacy-preserving approach to human-computer interaction. However, most existing research focuses predominantly on learning for a single task, which requires separate models for each task. This separation increases the complexity of the deployment and the computational overhead. To address these challenges, this study introduces a multi-task learning framework that simultaneously performs gesture recognition and person identification using a miniature radar sensor. By leveraging radar's capacity to capture finegrained spectral and spatial motion patterns, the framework incorporates micro-Doppler and range-Doppler processing, alongside a multi-branch architecture to enhance modality-specific feature representation. It enables unified learning of shared and task-specific features within a single network architecture. The proposed model, MTxLSTM, integrates CNN and the recent xLSTM to mitigate task interference, improve generalization, and improve gesture recognition through person-specific nuances while enhancing person identification by leveraging contextual gesture information. Experimental results reveal that MTxLSTM outperforms existing multi-task learning frameworks and stateof- the-art models, achieving 99.21% in gesture recognition and 98.59% in person identification with moderate model complexity and inference speed. This study concurrently executes gesture recognition and person identification using a miniature radar sensor, and marking the first application of xLSTM in radar sensing technology.