Abstract
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.Graphical abstractBiofeedback 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.
Highlights
Ultrasound imaging (USI) is a widely used technology in medicine and research
All detected contractions were seen with all three methods (USI, EMG, and FP), whereas for others, the contractions were so subtle that EMG and the force plate could not detect these muscle activations
For the instances when the detections differed between the methods, USI detections were reviewed to verify the presence of muscle activity and confirm that real contractions were detected
Summary
Ultrasound imaging (USI) is a widely used technology in medicine and research. It is a powerful tool, since it is noninvasive, cost effective, and portable, and it has the potential to objectively assess the functionality of muscles and assist in rehabilitation of patients recovering from a range of neuromuscular disorders [1, 2].In recent years there has been great interest in studying USI to characterize muscle activity since it enables visualization of the muscoskeletal system and evaluation of dimensional properties of the muscles at rest and during contraction [3,4,5,6,7].Many previous investigations involved manual assessment of US videos, including measurements of fascicle length and pennation angle, in a sequence of US images for analysis of muscle movement [8, 9]. Methods based on feature tracking between ultrasound images with optical flow or cross-correlation [12,13,14] and feature detection in a single US image [10, 15, 16] have been validated for several applications. These include detection of contracting muscle regions [15] and the study of various muscle architecture changes, such as cross-sectional area, muscle fascicle orientation [16], length [12, 16, 17], intra-fascicular strain, and shearing of aponeuroses [18]
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