Three Degree-of-freedom Modeling of the Random Fluctuation Arising in Human-bicycle Balance

Abstract

A novel three degree-of-freedom (DOF) fluctuation model that accurately reproduces the probability density functions (PDFs) of human-bicycle balance motions is proposed. We experimentally obtain the time series of the roll angular displacement, wheel's lateral displacement, steering angular displacement, and each velocity of them. Constructing the PDFs of these time series as training data, we identify the model parameters through the use of particle swarm optimization (PSO) that minimizes the squared residuals between the experimental participants' PDFs and those simulated by our model. Over 97% PDF fitnesses were obtained for all participants, indicating that our proposed model can successfully simulate the measured human PDFs.