Sharma Kavita, Kamatham Anne Tryphosa, Mukherjee Biswarup
IEEE Int Conf Rehabil Robot. 2025 May;2025:1-6. doi: 10.1109/ICORR66766.2025.11063209.
Sonomyography is a technique that uses ultrasound imaging to measure muscle activity. Unlike traditional methods, sonomyography allows for real-time imaging of deeper muscles. Recent research has focused on developing wearable ultrasound devices to enable real-time muscle activity sensing, specifically for applications in proportional control systems. By tracking muscle movement intensity and dynamics, wearable sonomyography systems enable precise control in real-time applications, such as target tracking and robotic prosthetics, improving the functionality and user-friendliness of biomechatronic interfaces. In this paper, we explore the proportional control for target tracking using the sonomyography (SMG) signals, captured through a sensor array. We evaluated three feature extraction methods for comparison: peak location-based features, amplitude histogram features, and curve-fitting features. These features were used to train a regression model, which we evaluated for its effectiveness in controlling real-time gestures. The results demonstrate that the amplitude histogram feature set achieved the highest accuracy of $88.72 \pm 3.27 %$ when trained with an ensemble regression model during the target tracking proportional control task using the SMG signal.
超声肌动图是一种利用超声成像来测量肌肉活动的技术。与传统方法不同，超声肌动图能够对深层肌肉进行实时成像。近期的研究集中在开发可穿戴超声设备，以实现实时肌肉活动传感，特别是在比例控制系统中的应用。通过跟踪肌肉运动强度和动态变化，可穿戴超声肌动图系统能够在诸如目标跟踪和机器人假肢等实时应用中实现精确控制，提高生物机电接口的功能和用户友好性。在本文中，我们探索了使用通过传感器阵列捕获的超声肌动图（SMG）信号进行目标跟踪的比例控制。我们评估了三种用于比较的特征提取方法：基于峰值位置的特征、幅度直方图特征和曲线拟合特征。这些特征被用于训练一个回归模型，我们评估了该模型在控制实时手势方面的有效性。结果表明，在使用SMG信号进行目标跟踪比例控制任务期间，当使用集成回归模型进行训练时，幅度直方图特征集实现了最高精度，为88.7±3.27%。
