Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020, Vol.2020, p.4775-4778

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.