Classifying Muscle States with One-Dimensional Radio-Frequency Signals from Single Element Ultrasound Transducers

• Main Authors: Brausch, L. (Author), Hewener, H. (Author), Lukowicz, P. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: machine learning; Machine learning models; Muscle; muscle contractions; Muscle contractions; muscle fatigue; Muscle fatigues; One-dimensional; Radio waves; radio-frequency signals; Radiofrequency signals; Reliable assessment; Single element; Sonomyography; Support vector machines; time series classification; Time series classifications; Ultrasonic applications; Ultrasonic transducers; ultrasound; Ultrasound transducers; Wearable technology; wearables
• Online Access: View Fulltext in Publisher

 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.