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使用可穿戴式 A 型超声估计动态活动中的关节扭矩。

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound.

机构信息

John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Mechanical and Industrial Engineering, University of Massachusetts Lowell, Lowell, MA, USA.

出版信息

Nat Commun. 2024 Jul 9;15(1):5756. doi: 10.1038/s41467-024-50038-0.


DOI:10.1038/s41467-024-50038-0
PMID:38982087
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11233567/
Abstract

The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

摘要

人体不断经受机械加载。然而,在肌肉骨骼系统内量化内部负荷仍然具有挑战性,尤其是在不受约束的动态活动期间。传统的测量方法受限于实验室环境,而现有的可穿戴方法在动态运动期间缺乏肌肉特异性或验证。在这里,我们提出了一种使用可穿戴式 A 型超声估计不同架构的肌肉在各种动态活动下相应关节扭矩的策略。我们首先介绍了一种使用单元素超声传感器跟踪肌肉厚度变化的方法。然后,我们在受控等速收缩期间以小于 7.6%的误差和大于 0.92 的确定系数(R)来估计肘部和膝盖的扭矩。最后,我们演示了在动态真实任务中(包括举重、骑自行车以及跑步机和户外步行)的可穿戴关节扭矩估计。在不受约束的真实世界活动中评估关节扭矩的能力可以为肌肉功能和运动生物力学提供新的见解,在损伤预防和康复方面具有潜在的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/7f62841290cc/41467_2024_50038_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/543dd8964642/41467_2024_50038_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/6209fdd45aa7/41467_2024_50038_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/021f1229b889/41467_2024_50038_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/106c00ee7101/41467_2024_50038_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/7f62841290cc/41467_2024_50038_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/543dd8964642/41467_2024_50038_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/6209fdd45aa7/41467_2024_50038_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/021f1229b889/41467_2024_50038_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/106c00ee7101/41467_2024_50038_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fe2/11233567/7f62841290cc/41467_2024_50038_Fig5_HTML.jpg

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本文引用的文献

[1]
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J Biomech. 2023-6

[2]
A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects.

Nat Biotechnol. 2024-3

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A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network.

IEEE Trans Neural Syst Rehabil Eng. 2023

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Sensors (Basel). 2023-2-3

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Sensors (Basel). 2022-12-1

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Sci Robot. 2022-10-26

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A Multimodal Multilevel Converged Attention Network for Hand Gesture Recognition With Hybrid sEMG and A-Mode Ultrasound Sensing.

IEEE Trans Cybern. 2023-12

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IEEE Trans Neural Syst Rehabil Eng. 2022

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Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring.

IEEE Trans Neural Syst Rehabil Eng. 2022

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Bioadhesive ultrasound for long-term continuous imaging of diverse organs.

Science. 2022-7-29

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