Automated semi-real-time detection of muscle activity with ultrasound imaging.

Ultrasound imaging (USI) biofeedback is a useful therapeutic tool; however, it relies on qualitative assessment by a trained therapist, while existing automatic analysis techniques are computationally demanding. This study aims to present a computationally inexpensive algorithm based on the differen...

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Publicado en:Medical & Biological Engineering & Computing Vol. 59; no. 9; pp. 1961 - 1972
Autores principales: Sosnowska, Anna J., Vuckovic, Aleksandra, Gollee, Henrik
Formato: Journal Article
Publicado: Springer Nature Sep2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2021
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-021-02407-w
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        atl: Automated semi-real-time detection of muscle activity with ultrasound imaging.
      aug:
        au:
          Sosnowska, Anna J.
          Vuckovic, Aleksandra
          Gollee, Henrik
        affil: School of Engineering, University of Glasgow, G12 8QQ, Glasgow, UK
      sug:
        subj:
          Muscle Contraction
          Isometric Contraction
          Muscle, Skeletal
          Electromyography
          Ultrasonography
          Arthritis Impact Measurement Scales
      ab: Ultrasound imaging (USI) biofeedback is a useful therapeutic tool; however, it relies on qualitative assessment by a trained therapist, while existing automatic analysis techniques are computationally demanding. This study aims to present a computationally inexpensive algorithm based on the difference in pixel intensity between USI frames. During an offline experiment, where data was analyzed after the study, participants performed isometric contractions of the gastrocnemius medialis (GM) muscle, as executed (30% of maximum contraction) or attempted (low force contraction up to a point when the participant is aware of exerting force or contracting the muscle) movements, while USI, EMG, and force data were recorded. The algorithm achieved 99% agreement with EMG and force measurements for executed movements and 93% for attempted movements, with USI detecting 1.9% more contractions than the other methods. In the online study, participants performed GM muscle contractions at 10% and 30% of maximum contraction, while the algorithm provided visual feedback proportional to the muscle activity (based on USI recordings during the maximum contraction) in less than 3 s following each contraction. We show that the participants reached the target consistently, learning to perform precise contractions. The algorithm is reliable and computationally very efficient, allowing real-time applications on standard computing hardware. It is a suitable method for automated detection, quantification of muscle contraction, and to provide biofeedback which can be used for training of targeted muscles, making it suitable for rehabilitation. Biofeedback session based on ultrasound imaging (USI) during muscle training. Novel, computationally inexpensive algorithm based on the difference in pixel intensity between USI frames is used to process the video and provide quantitative feedback on the strength of muscle contraction.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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