Three degree-of-freedom stochastic modeling of human-bicycle balance fluctuations

Abstract

In this study, we propose a new three degree-of-freedom (DOF) fluctuation model that accurately reproduces the probability density functions (PDFs) of human-bicycle balance motions as simply as possible. First, we measure the PDFs of the roll angular displacement, wheel’s lateral displacement, steering angular displacement, and each velocity. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization (PSO); in particular, we minimize the squared residuals between the experimental PDFs from the participants and our simulated PDFs. The resulting PDF fitnesses were over 97% for all participants, indicating that our simulated PDFs reproduced the human PDFs.