GEGLU-Transformer for IMU-to-EMG Estimation with Few-Shot Adaptation

• URL: http://arxiv.org/abs/2604.25670v1
• Date: Tue, 28 Apr 2026 14:02:54 GMT
• Title: GEGLU-Transformer for IMU-to-EMG Estimation with Few-Shot Adaptation
• Abstract summary: We propose an adaptive IMU-to-EMG learning framework that reconstructs continuous muscle activation envelopes from wearable inertial measurements.<n>Results support attention-based architectures combined with lightweight adaptation as a practical and scalable alternative to direct EMG sensing.
• Score: 3.2245472558257315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable estimation of neuromuscular activation is a key enabler for adaptive and personalized control in wearable robotics. However, surface electromyography (EMG) remains difficult to deploy robustly outside laboratory settings due to electrode sensitivity, signal non-stationarity, and strong subject dependence. In this work, we propose an adaptive IMU-to-EMG learning framework that reconstructs continuous muscle activation envelopes from wearable inertial measurements across heterogeneous movement conditions. The approach combines a Transformer encoder with Gaussian Error Gated Linear Units (GEGLU-Transformer) to enhance cross-subject generalization and enable rapid subject-specific personalization. Under a strict leave-one-subject-out (LOSO) protocol on a multi-condition lower-limb biomechanics dataset, the proposed architecture achieves r = 0.706 +/- 0.139 and R^2 = 0.474 +/- 0.208 without subject-specific adaptation. With only 0.5% adaptation data, performance increases to r = 0.761 +/- 0.030 and R^2 = 0.559 +/- 0.047, demonstrating rapid adaptation and early performance saturation. These results support attention-based architectures combined with lightweight adaptation as a practical and scalable alternative to direct EMG sensing for real-world wearable robotic applications.

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