Estimating fall probability in cycling

Estimating fall probability in cycling: Prediction of the effectiveness of a balance-assist bicycle in reducing falls

Haitjema, Marten (TU Delft Mechanical, Maritime and Materials Engineering)

Moore, J.K. (mentor)
Alizadehsaravi, L. (graduation committee)

Delft University of Technology

Mechanical Engineering | BioMechanical Design

2023-12-14

Most of the injuries sustained by cyclists are due to accidents that do not involve other road-users. This type of accident is known as a single-bicycle accident. A bicycle that is more stable may help to reduce the number of single-bicycle accidents. A balance-assist bicycle is developed at the TU Delft in collaboration with Royal Dutch Gazelle and Bosch eBike Systems. This bicycle has a motor that applies a torque about the steering axis. The motor automatically steers into the direction of the fall - the primary method of controlling a bicycle. The goal of the balance-assist bicycle is reducing the amount of single-bicycle accidents by increasing the stability of the bicycle.

The first research goal of this thesis is to characterize the dynamics of the balance-assist bicycle. Experiments are performed at multiple speeds and controller gains in which the riderless balance-assist bicycle is perturbed and the consequent roll rate is measured. A function describing a decaying oscillation is fitted to this data to retrieve the eigenvalues. A Whipple-Carvallo parameter set is created that approximates the measured dynamics of the bicycle. The results show that the balance-assist controller is not able to stabilize the bicycle when the mass and inertia of a rigid rider is added. However, the controller does damp the weave mode substantially. This likely makes it easier for an active rider to control the bicycle.

The second research goal is to create a model based on experimental data that predicts what perturbations to the rider-bicycle system result in a fall, and what perturbations do not result in a fall. An experimental setup is created that can apply perturbations to the bicycle in a safe environment. The experimental set up consists of four motors that pull on ropes attached to load cells at the end of the bicycle's handlebars. Experiments take place on a treadmill surrounded by padding. Participants wear a safety harness suspended from the ceiling. An operator controlling the treadmill stops the treadmill when a participant falls. The four motors are able to apply perturbations of different magnitudes and direction to the handlebars of the bicycle. The goal of the experiments is to apply perturbations for which a participant falls approximately 50\% of the
time. The controller applying the perturbations and internal sensors of the balance-assist bicycle collect data.

A Bayesian and a frequentist multilevel logistic regression models are developed to predict the probability that a perturbation results in a fall. Results show that there are two predictor variables that have a significant influence on fall probability. Firstly and most importantly, an increase in angular impulse increases the probability that the cyclist falls. Secondly, the fall probability decreases if a participants has experienced more perturbations.

The created model is used to predict the effect that the balance-assist controller has on the fall probability. This shows that the controller could substantially reduce this probability. Moreover, this thesis shows that the balance-assist bicycle substantially dampens the weave mode of the balance-assist bicycle when the mass and inertia of a rigid rider are added. This likely makes it easier for a rider to recover from a perturbation. So, results from this thesis show that, in theory, the balance-assist bicycle could reduce fall probability. The experimental set up, protocol and data analysis scripts developed in this thesis provide an excellent starting point to validate the effectiveness of the balance-assist bicycle in future work.

cycling
fall prevention

http://resolver.tudelft.nl/uuid:b7a3ff8d-de2b-47ee-ac05-61c34c1eac23

2025-12-14

Student theses

master thesis

© 2023 Marten Haitjema