The kinematics of cyclic human movement
| Autor: | Manfred Vieten, Christian Weich |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Kinematics Computer science Science Acceleration Research and Analysis Methods Running Young Adult Motion Mathematical and Statistical Techniques cyclic motion kinematics attractor gait applied mathematics computer simulation Attractor Medicine and Health Sciences Humans Computer Simulation ddc:796 Musculoskeletal System Mathematical Models Movement (music) Physics Simulation and Modeling Biology and Life Sciences Classical Mechanics Function (mathematics) Noise Reduction Bicycling Biomechanical Phenomena Morphing Random Walk Physical Sciences Signal Processing Medicine Engineering and Technology Female Noise (video) Anatomy Electronics Accelerometers Algorithm Research Article |
| Zdroj: | PLoS ONE PLoS ONE, Vol 15, Iss 3, p e0225157 (2020) |
| DOI: | 10.1101/826313 |
| Popis: | Literature mentions two types of models describing cyclic movement-theory and data driven. Theory driven models include anatomical and physiological aspects. They are principally suitable for answering questions about the reasons for movement characteristics, but they are complicated and substantial simplifications do not allow generally valid results. Data driven models allow answering specific questions, but lack the understanding of the general movement characteristic. With this paper we try a compromise without having to rely on anatomy, neurology and muscle function. We hypothesize a general kinematic description of cyclic human motion is possible without having to specify the movement generating processes, and still get the kinematics right. The model proposed consists of a superposition of six contributions-subject's attractor, morphing, short time fluctuation, transient effect, control mechanism and sensor noise, while characterizing numbers and random contributions. We test the model with data from treadmill running and stationary biking. Applying the model in a simulation results in good agreement between measured data and simulation values. We find in all our cases the similarity analysis between measurement and simulation is best for the same subjects-[Formula: see text] and [Formula: see text]. All comparisons between different subjects are [Formula: see text] and [Formula: see text]. This uniquely allows for the identification of each measurement for the associated simulation. However, even different subject comparisons show good agreement between measurement and simulation results of differences δrun = 6.7±4.7% and δbike = 5.1±4.5%. published |
| Databáze: | OpenAIRE |
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