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 (R2 = 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.