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Continuous Estimation of Achilles Tendon Loading in Rupture Patients Using a Single Boot-Mounted Accelerometer
Summary
A new model uses a boot-mounted accelerometer to estimate Achilles tendon loading during early recovery from rupture. This allows for continuous monitoring and personalized rehabilitation guidance for patients.
Area of Science:
- Biomechanics
- Orthopedic Rehabilitation
- Machine Learning in Healthcare
Background:
- Achilles tendon ruptures cause lasting functional impairments.
- Current rehabilitation lacks continuous monitoring of tendon loading.
- Previous research was limited by the inability to track Achilles tendon load during recovery.
Purpose of the Study:
- Develop a data-driven model to predict Achilles tendon loading.
- Utilize a single, boot-mounted accelerometer for load estimation.
- Enable continuous monitoring of tendon loading during early rehabilitation.
Main Methods:
- Collected walking trial data from 19 Achilles rupture patients in instrumented boots.
- Employed a boot-mounted inertial measurement unit for acceleration data.
- Developed a stance detection algorithm and a personalized 1D-CNN for load prediction.
Main Results:
- Stance detection achieved 99.8% precision.
- The 1D-CNN model estimated peak Achilles tendon load with a mean absolute error of 0.14 bodyweights (R²=0.68).
- Model errors were approximately 9% relative to the observed loading range.
Conclusions:
- Continuous, objective estimation of Achilles tendon loading is feasible with a single accelerometer.
- The model provides a foundation for personalized rehabilitation strategies post-Achilles rupture.
- Enables clinicians to monitor mechanical loading during a critical, previously unobservable rehabilitation phase.
