Multi‐command wearable magnetic human‐machine interface enabled by low‐noise sensing and time‐series deep learning

Wearable magnetic human‐machine interfaces offer unique advantages for unobtrusive and robust interaction, yet their practical deployment is limited by two fundamental challenges: rapid signal attenuation with distance at the hardware level (low signal‐to‐noise ratios) and ambiguous signal interpretation at the algorithmic level. Here, we present a multi‐command magnetic HMI that synergistically integrates low‐noise sensor engineering with time‐series deep learning to overcome these limitations. At the device level, we realized a planar Hall magnetoresistive sensor with an optimized concentric‐ring configuration and a low‐noise readout architecture, achieving a noise floor approaching the thermal limit. The system resolves magnetic‐field variations down to 0.4 μT corresponding to 2 mm displacement at a 12 cm sensor‐magnet separation while extending the interaction range to 20 cm. At the algorithmic level, we developed a temporal learning framework and systematically compared thresholding, a rule‐based classifier, multilayer perceptron, 1D convolutional neural network (1D‐CNN), long short‐term memory (LSTM), and several modern sequence‐learning architectures. The LSTM model achieved an average accuracy of 99.4% for four gesture primitives and maintained 99.3% accuracy after extending the gesture set to six commands by introducing two temporally more complex gestures. Augmentation‐enhanced training further improved performance across acquisition days, previously unseen users, wearing‐position variations, environmental disturbances, and motion artifacts, with accuracies exceeding 90% under all evaluated conditions, demonstrating the system's generalization capability and practical robustness. By combining near‐thermal‐noise magnetic sensing with temporal learning, this work establishes a scalable framework for long‐range, high‐resolution wearable interaction.