紧绷到断裂：利用突发振动实现跟腱负荷的无创估计
Fit to Burst: Toward Noninvasive Estimation of Achilles Tendon Load Using Burst Vibrations.
作者信息
Bolus Nicholas B, Jeong Hyeon Ki, Blaho Bradley M, Safaei Mohsen, Young Aaron J, Inan Omer T
出版信息
IEEE Trans Biomed Eng. 2021 Feb;68(2):470-481. doi: 10.1109/TBME.2020.3005353. Epub 2021 Jan 21.
OBJECTIVE
Tendons are essential components of the musculoskeletal system and, as with any mechanical structure, can fail under load. Tendon injuries are common and can be debilitating, and research suggests that a better understanding of their loading conditions could help mitigate injury risk and improve rehabilitation. To that end, we present a novel method of noninvasively assessing parameters related to mechanical load in the Achilles tendon using burst vibrations.
METHODS
These vibrations, produced by a small vibration motor on the skin superficial to the tendon, are sensed by a skin-mounted accelerometer, which measures the tendon's response to burst excitation under varying tensile load. In this study, twelve healthy subjects performed a variety of everyday tasks designed to expose the Achilles tendon to a range of loading conditions. To approximate the vibration motor-tendon system and provide an explanation for observed changes in tendon response, a 2-degree-of-freedom mechanical systems model was developed.
RESULTS
Reliable, characteristic changes in the burst response profile as a function of Achilles tendon tension were observed during all loading tasks. Using a machine learning-based approach, we developed a regression model capable of accurately estimating net ankle moment-which captures general trends in tendon tension-across a range of walking speeds and across subjects (R = 0.85). Simulated results of the mechanical model accurately recreated behaviors observed in vivo. Finally, preliminary, proof-of-concept results from a fully wearable system demonstrated trends similar to those observed in experiments conducted using benchtop equipment.
CONCLUSION
These findings suggest that an untethered, unobtrusive system can effectively assess tendon loading during activities of daily life.
SIGNIFICANCE
Access to such a system would have broad implications for injury recovery and prevention, athletic training, and the study of human movement.
目的
肌腱是肌肉骨骼系统的重要组成部分，与任何机械结构一样，在负荷下可能会失效。肌腱损伤很常见且可能使人衰弱，研究表明，更好地了解其负荷状况有助于降低受伤风险并改善康复效果。为此，我们提出了一种使用突发振动无创评估跟腱机械负荷相关参数的新方法。
方法
由位于肌腱表面皮肤处的小型振动电机产生的这些振动，由安装在皮肤上的加速度计感知，该加速度计测量肌腱在不同拉伸负荷下对突发激励的反应。在本研究中，12名健康受试者执行了各种日常任务，旨在使跟腱暴露于一系列负荷条件下。为了近似振动电机 - 肌腱系统并解释观察到的肌腱反应变化，开发了一个二自由度机械系统模型。
结果
在所有负荷任务中，均观察到突发反应曲线随跟腱张力的可靠特征变化。使用基于机器学习的方法，我们开发了一个回归模型，能够准确估计净踝关节力矩——该力矩反映了一系列步行速度和不同受试者之间肌腱张力的总体趋势（R = 0.85）。机械模型的模拟结果准确地重现了体内观察到的行为。最后，一个完全可穿戴系统的初步概念验证结果显示出与使用台式设备进行的实验中观察到的趋势相似。
结论
这些发现表明，一个无需系绳、不引人注意的系统可以有效评估日常生活活动期间的肌腱负荷。
意义
获得这样一个系统将对损伤恢复和预防、运动训练以及人体运动研究产生广泛影响。
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