{"funded_apc":"1","department":[{"_id":"103"}],"date_updated":"2024-02-06T11:02:55Z","article_number":"e0289549","language":[{"iso":"eng"}],"intvolume":"        18","main_file_link":[{"open_access":"1"}],"type":"journal_article","abstract":[{"text":"                  \r\n                    For assistive devices such as active orthoses, exoskeletons or other close-to-body robotic-systems, the immediate prediction of biological limb movements based on biosignals in the respective control system can be used to enable intuitive operation also by untrained users e.g. in healthcare, rehabilitation or industrial scenarios. Surface electromyography (sEMG) signals from the muscles that drive the limbs can be measured before the actual movement occurs and, hence, can be used as source for predicting limb movements. The aim of this work was to create a model that can be adapted to a new user or movement scenario with little measurement and computing effort. Therefore, a biomechanical model is presented that predicts limb movements of the human forearm based on easy to measure sEMG signals of the main muscles involved in forearm actuation (\r\n                    lateral\r\n                    and\r\n                    long head\r\n                    of\r\n                    triceps\r\n                    and\r\n                    short\r\n                    and\r\n                    long head\r\n                    of\r\n                    biceps\r\n                    ). The model has 42 internal parameters of which 37 were attributed to 8 individually measured physiological measures (location of\r\n                    acromion\r\n                    at the shoulder,\r\n                    medial/lateral epicondyles\r\n                    as well as\r\n                    olecranon\r\n                    at the elbow, and\r\n                    styloid processes\r\n                    of\r\n                    radius/ulna\r\n                    at the wrist; maximum muscle forces of\r\n                    biceps\r\n                    and\r\n                    triceps\r\n                    ). The remaining 5 parameters are adapted to specific movement conditions in an optimization process. The model was tested in an experimental study with 31 subjects in which the prediction quality of the model was assessed. The quality of the movement prediction was evaluated by using the normalized mean absolute error (nMAE) for two arm postures (lower, upper), two load conditions (2 kg, 4 kg) and two movement velocities (slow, fast). For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons. This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality.\r\n                  \r\n                ","lang":"eng"}],"publication_identifier":{"eissn":["1932-6203"]},"doi":"10.1371/journal.pone.0289549","volume":18,"quality_controlled":"1","oa":"1","publisher":"Public Library of Science (PLoS)","issue":"8","author":[{"first_name":"Nils","orcid":"0000-0002-4864-4978","id":"214493","full_name":"Grimmelsmann, Nils","last_name":"Grimmelsmann"},{"last_name":"Mechtenberg","full_name":"Mechtenberg, Malte","id":"218573","orcid":"0000-0002-8958-0931","first_name":"Malte"},{"full_name":"Schenck, Wolfram","last_name":"Schenck","first_name":"Wolfram","orcid":"0000-0003-3300-2048","id":"224375"},{"id":"231466","first_name":"Hanno Gerd","orcid":"0000-0003-2454-3897","last_name":"Meyer","full_name":"Meyer, Hanno Gerd"},{"id":"213480","first_name":"Axel","orcid":"0000-0002-6632-3473","last_name":"Schneider","full_name":"Schneider, Axel"}],"_id":"3453","article_type":"original","status":"public","title":"sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters","date_created":"2023-08-22T09:51:09Z","publication":"PLOS ONE","project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"},{"_id":"72dfeb62-b436-11ed-9513-f39505d26204","name":"CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie"}],"citation":{"short":"N. Grimmelsmann, M. Mechtenberg, W. Schenck, H.G. Meyer, A. Schneider, PLOS ONE 18 (2023).","alphadin":"<span style=\"font-variant:small-caps;\">Grimmelsmann, Nils</span> ; <span style=\"font-variant:small-caps;\">Mechtenberg, Malte</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span> ; <span style=\"font-variant:small-caps;\">Meyer, Hanno Gerd</span> ; <span style=\"font-variant:small-caps;\">Schneider, Axel</span>: sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. In: <i>PLOS ONE</i> Bd. 18, Public Library of Science (PLoS) (2023), Nr. 8","ieee":"N. Grimmelsmann, M. Mechtenberg, W. Schenck, H. G. Meyer, and A. Schneider, “sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters,” <i>PLOS ONE</i>, vol. 18, no. 8, 2023.","chicago":"Grimmelsmann, Nils, Malte Mechtenberg, Wolfram Schenck, Hanno Gerd Meyer, and Axel Schneider. “SEMG-Based Prediction of Human Forearm Movements Utilizing a Biomechanical Model Based on Individual Anatomical/ Physiological Measures and a Reduced Set of Optimization Parameters.” <i>PLOS ONE</i> 18, no. 8 (2023). <a href=\"https://doi.org/10.1371/journal.pone.0289549\">https://doi.org/10.1371/journal.pone.0289549</a>.","apa":"Grimmelsmann, N., Mechtenberg, M., Schenck, W., Meyer, H. G., &#38; Schneider, A. (2023). sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. <i>PLOS ONE</i>, <i>18</i>(8). <a href=\"https://doi.org/10.1371/journal.pone.0289549\">https://doi.org/10.1371/journal.pone.0289549</a>","bibtex":"@article{Grimmelsmann_Mechtenberg_Schenck_Meyer_Schneider_2023, title={sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters}, volume={18}, DOI={<a href=\"https://doi.org/10.1371/journal.pone.0289549\">10.1371/journal.pone.0289549</a>}, number={8e0289549}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Grimmelsmann, Nils and Mechtenberg, Malte and Schenck, Wolfram and Meyer, Hanno Gerd and Schneider, Axel}, year={2023} }","ama":"Grimmelsmann N, Mechtenberg M, Schenck W, Meyer HG, Schneider A. sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters. <i>PLOS ONE</i>. 2023;18(8). doi:<a href=\"https://doi.org/10.1371/journal.pone.0289549\">10.1371/journal.pone.0289549</a>","mla":"Grimmelsmann, Nils, et al. “SEMG-Based Prediction of Human Forearm Movements Utilizing a Biomechanical Model Based on Individual Anatomical/ Physiological Measures and a Reduced Set of Optimization Parameters.” <i>PLOS ONE</i>, vol. 18, no. 8, e0289549, Public Library of Science (PLoS), 2023, doi:<a href=\"https://doi.org/10.1371/journal.pone.0289549\">10.1371/journal.pone.0289549</a>."},"user_id":"213480","publication_status":"published","year":"2023"}