Matthew G Yough, Russell L Hardesty, Sergiy Yakovenko, Valeriya Gritsenko
{"title":"A segmented forearm model of hand pronation-supination approximates joint moments for real time applications.","authors":"Matthew G Yough, Russell L Hardesty, Sergiy Yakovenko, Valeriya Gritsenko","doi":"10.1109/ner49283.2021.9441405","DOIUrl":null,"url":null,"abstract":"<p><p>Musculoskeletal modeling is a new computational tool to reverse engineer human control systems, which require efficient algorithms running in real-time. Human hand pronation-supination movement is accomplished by movement of the radius and ulna bones relative to each other via the complex proximal and distal radioulnar joints, each with multiple degrees of freedom (DOFs). Here, we report two simplified models of this complex kinematic transformation implemented as a part of a 20 DOF model of the hand and forearm. The pronation/supination DOF was implemented as a single rotation joint either within the forearm segment or separating proximal and distal parts of the forearm segment. Torques produced by the inverse dynamic simulations with anatomical architecture of the forearm (OpenSim model) were used as the \"gold standard\" in the comparison of two simple models. Joint placement was iteratively optimized to achieve the closest representation of torques during realistic hand movements. The model with a split forearm segment performed better than the model with a solid forearm segment in simulating pronation/supination torques. We conclude that simplifying pronation/supination DOF as a single-axis rotation between arm segments is a viable strategy to reduce the complexity of multi-DOF dynamic simulations.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":" ","pages":"751-754"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ner49283.2021.9441405","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ner49283.2021.9441405","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/6/2 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Musculoskeletal modeling is a new computational tool to reverse engineer human control systems, which require efficient algorithms running in real-time. Human hand pronation-supination movement is accomplished by movement of the radius and ulna bones relative to each other via the complex proximal and distal radioulnar joints, each with multiple degrees of freedom (DOFs). Here, we report two simplified models of this complex kinematic transformation implemented as a part of a 20 DOF model of the hand and forearm. The pronation/supination DOF was implemented as a single rotation joint either within the forearm segment or separating proximal and distal parts of the forearm segment. Torques produced by the inverse dynamic simulations with anatomical architecture of the forearm (OpenSim model) were used as the "gold standard" in the comparison of two simple models. Joint placement was iteratively optimized to achieve the closest representation of torques during realistic hand movements. The model with a split forearm segment performed better than the model with a solid forearm segment in simulating pronation/supination torques. We conclude that simplifying pronation/supination DOF as a single-axis rotation between arm segments is a viable strategy to reduce the complexity of multi-DOF dynamic simulations.