Classifying muscle states with ultrasonic single element transducer data using machine learning strategies

Brausch, Lukas; Hewener, Holger

doi:10.1121/2.0001140

Cited by 5 publications

(2 citation statements)

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“…Figure 1 illustrates the examined muscles or muscle groups with the gastrocnemius muscle sketched on the left and the biceps brachii muscle sketched on the right. This work builds upon and extends previously published preliminary results [ 22 , 23 ].…”

Section: Introductionsupporting

confidence: 84%

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

Brausch

Hewener

Lukowicz

2022

Sensors

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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.

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Section: Introductionsupporting

confidence: 84%

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

Brausch

Hewener

Lukowicz

2022

Sensors

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“…In particular, the third requirement allows the use of analysis methods beyond pure image-based segmentation and classification. We recently showed in other applications that machine learning approaches can be applied to raw radio-frequent ultrasound data prior to image formation for classification tasks with a high accuracy [ 4 ]. Radio-frequent data with a high dynamic range (16-bit amplitude quantization) and ultrasonic wave phase information at high digitalization rates of up to 50 MHz contain a lot more informational content than scan-converted ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Fournelle

Grün

Speicher

et al. 2021

Sensors

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We developed a new mobile ultrasound device for long-term and automated bladder monitoring without user interaction consisting of 32 transmit and receive electronics as well as a 32-element phased array 3 MHz transducer. The device architecture is based on data digitization and rapid transfer to a consumer electronics device (e.g., a tablet) for signal reconstruction (e.g., by means of plane wave compounding algorithms) and further image processing. All reconstruction algorithms are implemented in the GPU, allowing real-time reconstruction and imaging. The system and the beamforming algorithms were evaluated with respect to the imaging performance on standard sonographical phantoms (CIRS multipurpose ultrasound phantom) by analyzing the resolution, the SNR and the CNR. Furthermore, ML-based segmentation algorithms were developed and assessed with respect to their ability to reliably segment human bladders with different filling levels. A corresponding CNN was trained with 253 B-mode data sets and 20 B-mode images were evaluated. The quantitative and qualitative results of the bladder segmentation are presented and compared to the ground truth obtained by manual segmentation.

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Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data

Hafthorsdottir¹,

Vostrikov²,

Cossettini³

et al. 2022

2022 IEEE Sensors Applications Symposium (SAS)

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Classifying muscle states with ultrasonic single element transducer data using machine learning strategies

Cited by 5 publications

References 0 publications

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

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

Portable Ultrasound Research System for Use in Automated Bladder Monitoring with Machine-Learning-Based Segmentation

Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data

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