Comparative Study of a Biomechanical Model-based and Black-box Approach for Subject-Specific Movement Prediction

• Published in: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 4775 - 4778
• Main Authors: Walter, Johannes R., Saini, Harnoor, Maier, Benjamin, Mostashiri, Naser, Aguayo, Jaime L., Zarshenas, Homayoon, Hinze, Christoph, Shuva, Shahnewaz, Kohler, Johannes, Sahrmann, Annika S., Chang, Che-ming, Csiszar, Akos, Galliani, Simona, Cheng, Leo K., Rohrle, Oliver
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• Subjects: Biological system modeling; Data models; Elbow; Electromyography; Muscles; Predictive models; Torque
• ISSN: 2694-0604
• DOI: 10.1109/EMBC44109.2020.9176600

The performance and safety of human robot interaction (HRI) can be improved by using subject-specific movement prediction. Typical models include biomechanical (parametric) or black-box (non-parametric) models. The current work aims to investigate the benefits and drawbacks of these approaches by comparing elbow-joint torque predictions based on electromyography signals of the elbow flexors and extensors. To this end, a parameterized biomechanical model is compared to a non-parametric (Gaussian-process) approach. Both models showed adequate results in predicting the elbow-joint torques. While the non-parametric model requires minimal modeling effort, the parameterized biomechanical model can lead to deeper insight of the underlying subject specific musculoskeletal system.