论文标题
射线镜检查（RMG）的新型肌肉监测和手势识别的应用
Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition
论文作者
论文摘要
常规肌电图（EMG）测量肌肉收缩期间的连续神经活动，但缺乏对实际收缩的明确定量。机械学（MMG）和加速度计仅测量身体表面运动，而超声，CT-SCAN和MRI仅限于临界快照。在这里，我们提出了一种新型的放射学（RMG），用于连续肌肉致动，该传感可能是可穿戴和无触摸的，可以捕获浅表和深肌肉群。我们通过前臂可穿戴传感器实验验证了RMG，以详细的手势识别。我们首先将无线电传感输出转换为时频谱图，然后将视觉变压器（VIT）深度学习网络作为分类模型，可以识别8个受试者的平均准确性高达99％的23个手势。通过转移学习，对用户差异和传感器变化的高适应性的平均准确性高达97％。我们进一步证明了RMG以监测眼睛和腿部肌肉，并获得了高精度的眼动和身体姿势跟踪。 RMG可以与同步EMG一起使用，以在运动机能学，物理疗法，康复和人机界面中的许多未来应用中得出刺激 - 实现波形。
Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.
