Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video - Zhengliang Xia; Bradley M. Cornish; Daniel Devaprakash; Rod S. Barrett; David G. Lloyd; Andrea H. Hams; Claudio Pizzolato (IEEE, 2024)
Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) $\le 526$ N, normalized RMSE (nRMSE) $\le 0.21$ , R $^{{2}} \ge 0.81$ . Walking task resulted the most accurate with RMSE $= 189± 62$ N; nRMSE $= 0.11± 0.03$ , R $^{{2}}= 0.92± 0.04$ . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.