Comparative Study of a Biomechanical Model-based and Black-box Approach for Subject-Specific Movement Prediction - 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; et al. (2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020)
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.