Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video

Abstract

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 $= 189\pm 62$ N; nRMSE $= 0.11\pm 0.03$ , R $^{{2}}= 0.92\pm 0.04$ . 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.

Keywords

• Long short-term memory (LSTM) neural network
• artificial intelligence (AI)
• pose estimation
• neu romusculoskeletal (NMSK) model
• Achilles tendon (AT)
• rehabilitation