Fit to Burst: Toward Noninvasive Estimation of Achilles Tendon Load Using Burst Vibrations

• Published in: IEEE transactions on biomedical engineering 2021-02, Vol.68 (2), p.470-481
• Main Authors: Bolus, Nicholas B., Jeong, Hyeon Ki, Blaho, Bradley M., Safaei, Mohsen, Young, Aaron J., Inan, Omer T.
• Format: Article
• Language: English
• Subjects: Accelerometers; Achilles Tendon; Ankle; Ankle Joint; Biomechanical Phenomena; burst response; Feature extraction; haptic; Health risks; Human motion; Humans; Injuries; Injury prevention; Learning algorithms; Loading; Machine learning; Mechanical properties; Mechanical systems; Musculoskeletal system; noninvasive; Regression models; Rehabilitation; Skin; Tendon Injuries; Tendons; Tensile stress; tension; Trends; Vibration; Vibrations; Walking; wearable
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2020.3005353

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 2 = 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.