Publication

Machine learning models are powerful tools for applications such as disease classification, but their effective- ness is often limited by the availability of data. To address this, we propose a novel data augmentation method utilizing neuromusculoskeletal simulations. Parameters fitted to gait data from Parkinson’s disease patients were estimated using a linear regression model derived from clinical scales, enabling the generation of augmented data. The proposed method successfully produced parameters similar as those obtained from actual data, and the neuromusculoskeletal model using these parameters was able to simulate gait. This suggests that the proposed method has potential for data augmentation.