Continuous Estimation of Achilles Tendon Loading in Rupture Patients Using a Single Boot-Mounted Accelerometer

Achilles tendon ruptures lead to long-term structural and functional deficits. Prior research that sought to identify optimal rehabilitation techniques was fundamentally limited by the inability to continuously monitor Achilles tendon loading during rehabilitation. Our objective was to develop a data-driven model that predicts per-step peak Achilles tendon loading from only a single, boot-mounted accelerometer.

Nineteen patients recovering from an acute Achilles tendon rupture completed in-lab walking trials while wearing an instrumented immobilizing boot. A boot-mounted inertial measurement unit provided acceleration signals used for prediction, while a force-sensing insole provided ground truth tendon-loading data through a validated ankle moment balance. We developed a stance detection algorithm, as well as a personalized one-dimensional convolutional neural network (1D-CNN) to estimate per-step peak Achilles tendon load. Our training framework incorporated a small patient-specific personalization sample and was evaluated on held-out steps.

The stance detection algorithm identified stance phases with 99.8% precision and mean timing differences of 27.3 ms for heel strike and 61.9 ms for toe off. The 1D-CNN estimated per-step peak Achilles tendon load with a mean absolute error of 0.14 bodyweights ( $\mathrm{R}^{{2}}=0.68$ ) across rupture patients.

Continuous, objective estimation of Achilles tendon loading during early rehabilitation is feasible using a single, boot-mounted accelerometer. Model errors were small (9%) relative to the wide range of tendon loading exhibited during immobilizing boot walking. Our proposed approach enables clinicians to continuously monitor mechanical loading during a previously unobservable rehabilitation period and provides a foundation for personalized rehabilitation guidance after Achilles rupture.