ArticleJournal of biomechanics2023
A trained neural network model accurately predicts Achilles tendon stress during walking and running based on shear wave propagation.
Article in Journal of biomechanics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners.NPJ digital medicine · 2025Article
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Abstract
Shear wave tensiometry is a noninvasive technique for measuring tendon loading during activity based on the speed of a shear wave traveling along the tendon. Shear wave speed has been shown to modulate with axial stress, but calibration is required to obtain absolute measures of tendon loading. However, the current technique only makes use of wave speed, whereas other characteristics of the wave (e.g., amplitude, frequency content) may also vary with tendon loading. It is possible that these data could be used in addition to wave speed to circumvent the need for calibration. Given the potential complex relationships to tendon loading, and the lack of an analytical model to guide the use of these data, it is sensible to use a machine learning approach. Here, we used an ensemble neural network approach to predict inverse dynamics estimates of Achilles tendon stress from shear wave tensiometry data collected in a prior study. Neural network-predicted stresses were highly correlated with stance phase inverse dynamics estimates for walking (R
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