Fraunhofer Institute for Biomedical Engineering (IBMT), Joseph-von-Fraunhofer-Weg 1, 66280 Sulzbach, Germany.
Chair of Embedded Intelligence, Technical University of Kaiserslautern, Gottlieb-Daimler-Straße 47, 67663 Kaiserslautern, Germany.
Sensors (Basel). 2022 Apr 5;22(7):2789. doi: 10.3390/s22072789.
The reliable assessment of muscle states, such as contracted muscles vs. non-contracted muscles or relaxed muscles vs. fatigue muscles, is crucial in many sports and rehabilitation scenarios, such as the assessment of therapeutic measures. The goal of this work was to deploy machine learning (ML) models based on one-dimensional (1-D) sonomyography (SMG) signals to facilitate low-cost and wearable ultrasound devices. One-dimensional SMG is a non-invasive technique using 1-D ultrasound radio-frequency signals to measure muscle states and has the advantage of being able to acquire information from deep soft tissue layers. To mimic real-life scenarios, we did not emphasize the acquisition of particularly distinct signals. The ML models exploited muscle contraction signals of eight volunteers and muscle fatigue signals of 21 volunteers. We evaluated them with different schemes on a variety of data types, such as unprocessed or processed raw signals and found that comparatively simple ML models, such as Support Vector Machines or Logistic Regression, yielded the best performance w.r.t. accuracy and evaluation time. We conclude that our framework for muscle contraction and muscle fatigue classifications is very well-suited to facilitate low-cost and wearable devices based on ML models using 1-D SMG.
肌肉状态的可靠评估，如收缩肌肉与非收缩肌肉、放松肌肉与疲劳肌肉，在许多运动和康复场景中至关重要，例如治疗措施的评估。这项工作的目标是部署基于一维（1-D）声触诊弹性成像（SMG）信号的机器学习（ML）模型，以促进低成本和可穿戴式超声设备的发展。1-D SMG 是一种非侵入性技术，使用 1-D 超声射频信号来测量肌肉状态，其优势在于能够从深层软组织层获取信息。为了模拟真实场景，我们并没有强调获取特别明显的信号。ML 模型利用了 8 名志愿者的肌肉收缩信号和 21 名志愿者的肌肉疲劳信号。我们在不同的数据类型上（例如未经处理或处理后的原始信号），使用不同的方案对它们进行了评估，发现相对简单的 ML 模型，如支持向量机或逻辑回归，在准确性和评估时间方面表现出最佳性能。我们得出结论，我们的肌肉收缩和肌肉疲劳分类框架非常适合使用 1-D SMG 基于 ML 模型的低成本和可穿戴设备。
