Bicycles connected to the internet present an opportunity for integrated accident detection and geolocation. Such a system can reduce the time it takes for help to arrive by automatically alerting predefined contacts with the location of the accident. I developed a systematic method for the practical implementation of bicycle accident detection in connected bicycles and present the performance of a prototype system. The method uses accelerometer and gyroscopic measurements as well as localization and velocity estimations. Supplementing existing research, a bicycle accident detection system is validated on normal cycling, edge cases, and three types of single bicycle accidents with constraints set by a bicycle manufacturer. Edge cases are movements of a bicycle that occur during regular usage, but can not be described by normal cycling. This method uses a data¬driven approach. For the prototype system, the input signals are collected during 71 different simulated accidents and 54 hours of normal cycling and edge cases. A three¬layer detection algorithm determines if an accident has occurred and sends the last known location to a set of predefined contacts. Multiple combinations of thresholds and classification algorithms are compared. This resulted in a prototype system with a K¬Nearest Neighbours classifier which detects 75% of accidents. Normal cycling and edge cases are correctly detected 99.997% of the time. From all warnings send, 85.7% are true accidents. The prototype system proves that the proposed method can be used to integrate reliable accident detection in connected bicycles. Bicycles with such a system automatically inform emergency contacts with a message containing the location of the accident, in a time where every second counts.

Bicycle simulator research has been the subject of considerable research, however, few of these attempts have integrated direct balance control and enough freedom of motion to deliver a real-world kinematic cycling experience. In this study, the B.I.K.E. (Bicycle Intrinsic Kinematics Emulator) system, a kinematic bicycle simulator, is developed with the purpose of letting its users experience realistic kinematic motion, which are: steer, roll, yaw and sway motions. This study validates the developed simulator by performing a kinematic comparison of bicycle motion among 15 participants of varying age and mass, and performs an initial subjective study to investigate effects common to indoor vehicle simulation. Manoeuvres performed by the participants are straight-line cycling, at low (5 km/h) to high (40 km/h) velocities, as well as performing a zig-zagging motion. The results show that users can successfully rely on existing bicycle skills to use the simulator. They also show that, in the kinematic sense, the simulator performs similarly to an outdoor bicycle, particularly at velocities below 35 km/h, but more work is needed in improving the vehicle model and control algorithm to accurately cover low to high-velocity cycling. Subjectively speaking, the simulator performs better than existing static solutions, but more work will be required to make the riding experience feel like real outdoor cycling.

Minimum-time optimisation has been used extensively in motorsports, such as Formula One racing. Using minimum-time optimisation, the ideal racing line as well as control strategies such as braking strategies can be found. This is interesting, as Reijne et al have shown that cyclists apply diverse strategies, specifically during a descent [1]. With minimum-time optimisation, it is possible to compare a riders performance to the theoretically optimal performance. The results from such optimisations can be used to help with training, as well as improve and test equipment design.

This work describes a free-trajectory steady motion control optimisation for the descent of elite cyclists. The prediction of the individual descent performance was formulated as an optimal control problem and solved with a direct approach to finding optimal cornering and braking strategies that yield the shortest descent time. While the state equations were kept simple (3 variables only), more elaborated performance limits were represented by g-g diagrams. Such diagrams represent the longitudinal, lateral, and combined acceleration limits for cyclists. A method to numerically derive g-g diagrams for cyclists driving on 3D tracks was designed. In this method, a tire model, power limit, and steady motion equations for a cyclist are used to determine the control space. The bicycle and cyclist are modeled as a single rigid body, the tire friction model is simplified as a friction circle, and the wind speed is considered to be zero at all times. As for the 3D road geometry effects, all possible effects are considered in the method, except lateral road curvature. The resulting method
provides g-g diagrams as a function of 8 local geometry and state variables.

The optimisation model was tested against the velocity and trajectory output data measured on Team Sunweb professional cyclists at the L218 descent in Germany. The resulting trajectory was similar to the trajectory ridden by elite cyclists. The velocity profile showed large differences, which are a result of a combination of inaccurate track data, differences in friction coefficient estimation, and safety margins applied by the cyclists. The results show that descent performance can be improved, as even when adhering to safety margins harder braking is possible. Overall, the model responds as expected to changes in track, environment, and bicycle/rider parameters.

Steps can be made towards better implementation of the g-g diagrams in the minimum-time optimisation. Furthermore, a more accurate tire model and power model can improve the model and extend its applications. The presented model can be used for qualitative descent analyses, and facilitate the training of elite cyclists.

[1] A.L. Schwab, M.M. Reijne, D.J.J. Bregman, Measuring and comparing descend in elite race cycling with a perspective on real-time feedback for improving individual performance. In Multidisciplinary Digital Publishing Institute Proceedings, volume 2, page 262, 2018.

Statistics show that cycling accidents have the biggest (and increasing) contribution to the overall number of hospital visits related to traffic accidents in the Netherlands. In the majority of these cycling accidents, no other road users are involved. Because the majority of the cycling accidents are so-called single vehicle accidents, understanding the control behaviour of the cyclist can spark new insight in to effective preventive measures. This thesis aims to achieve that by reviewing the current state-of-the-art in bicycle-rider control research and subsequently proposing a new rider control model that is developed using system identification techniques. The results of a literature review are used to propose a novel bicycle-rider control model structure that includes realistic human neural and cognitive characteristics to predict control behaviour for stabilizing a bicycle. Sensory integration is modelled with a Kalman filter, and human control and prediction capabilities are mimicked with a Linear-Quadratic-Regulator (LQR) and Tapped-Delay-Line predictor, respectively. Human neuromuscular dynamics are included in the model structure in the form of a second order filter and the bicycle dynamics are modelled with the linear Whipple bicycle model. Two different experimental data sets during which cyclists are laterally (roll) perturbed on instrumented bicycles are used: one collected at the UC Davis on both a horse treadmill and in a sports pavilion and the other collected at the TU Delft on a public cycling path (without other road users). The human steer response to the perturbation is separated from other effects caused by unknown disturbances and noise with a non-parametric Finite-Impulse-Response model. A structured system identification approach is used to determine the final free parameter set that approximates the non-parametric model in the best way possible. The data sets are split in a train set and a test set. The model structure is fitted to the train set. The predictive performance is verified by applying the resulting model to the test set. For the UC Davis data, the resulting model has one rider dependent parameter that describes human bicycle-balancing control behaviour for the entire evaluated forward velocity range (2-8 m/s). This parameter is the weight placed on the roll angle in the LQR control algorithm. The single-run training performance in terms of Variance-Accounted-For (VAF) with the non-parametric model reaches up to 97 %. Test performance incurred a VAF drop, on average, of around 10 %. The steer responses of the TU Delft experiment can be predicted with a model that has two rider dependent parameters: the weights placed on the roll angle and roll rate in LQR control algorithm. Training performance reaches up till 83 % and test performance incurred a VAF drop, on average, of around 7%. Promising future directions are the inclusion of a passive rider model to further increase the fidelity of the model. The investigation of the stability margins of the proposed bicycle-rider controller and their sensitivity to bicycle design and/or human sensory and cognitive decline can lead to tailored measures to reduce the injury rate among cyclists.

Part of the goal of Europe’s Strategy Vision Zero is to eliminate all severe cycling accidents in Europe by 2030. The majority of cycling accidents are single vehicle accidents; this term indicates the absence of collisions with other road participants and implies a fall of the cyclist. Research has shown that mainly elderly cyclists are victim to those accidents. An untested hypothesis is that their slowed motor responses are one of the main reasons behind this. This study aims to address this hypothesis by studying the influence of neural time delay on the lateral stability of bicycle-rider systems. In this study, the slowed motor responses are represented by single neural time delay values. The influence is quantified by the sizes of the identified basins of attraction of stability (BoA). The BoA contains the set of finite lateral disturbances for which stability is
retained and is acquired via numerical integration. Binary threshold criteria are used to determine the stability of the solution. The bicycle-rider system consists of two components: a bicycle model and a rider model. The bicycle is modelled using the Whipple(-Carvallo) bicycle model with the set of non-linear equations derived by Basu-Mandal [2]. The rider is modelled using an implicit experimentally validated model from literature [31]. This model consists of a PID controller with full state feedback, neuro-muscular dynamics and, in this study, is extended to include nonzero time delay. The neural time delay value of a young cyclist has been based on literature [4]. The value is doubled to model an older cyclist. The control strategy of the young cyclist is identified using system identification techniques. The basins of the young and old cyclist are compared to study the detrimental effect of time delay on lateral stability in cycling. It declined over 80% when the time delay was doubled. The human’s ability to adapt its control to circumstances has been considered by repeating the control identification process for the rider which suffers from double the time delay. With respect to the young cyclist, a decline of over 50% was observed. Therefore, the results strongly support the hypothesis. Further research should focus on increasing complexity of the rider model to include preview and prediction. In this way, the influence of slowed motor responses can be mapped more clearly.
A secondary objective of this thesis is the preliminary development of a steer assist control model to aid the elderly cyclist balance during cycling. This development builds further on a simple control model from literature [29] which uses roll angle feedback. As a result, a nonlinear velocity dependent roll rate feedback control law was developed. This control law yields a constant basin height over the commonly used velocity range of cyclists. This height indicates the maximum allowable steer rate perturbations the bicycle-steer assist system could handle and is approximately the same height as what was identified for
a young cyclist. Future research is required for improving the steer assist. This means adding maximum allowable control torque, sensorial time delays and trajectory tracking

The objective of this research is to identify the passive response of the rider’s body to translational and rotational random perturbations. A custom made bicycle mock-up equipped with a system of sensors has been developed, capable of measuring the linear accelerations, angular velocity and the rider’s force responses in all translational axes of all bicycle interfaces. The bicycle mock-up is driven by a hexapod that generates coloured noise perturbations in the range 0–10 Hz. Twenty four healthy male adults participated in this study and gave informed consent according to the guidelines of the ethical committee of Delft University of Technology. The responses of all subjects are represented in the frequency domain by means of frequency response functions. More specific, the interaction of the rider’s body at the seat, foot pegs and handlebars are expressed in terms of apparent mass and as seat-to-sternum transmissibility functions (STS). The apparent mass and STS transfer functions for the surge and heave motion suggest a simple underlying passive response system. For surge, a clear resonance peak was found at 2 Hz for nearly all interfaces and directions, whereas, for the heave motion a clear resonance peak at 5 Hz was found for the seat and handlebars and a resonance peak at 6 Hz for the foot pegs. The apparent mass of the pitch and yaw motion also suggest, to a certain extend, simple passive dynamics after 1 Hz characterised by resonance peaks at 1.8 and 2.3 Hz, respectively. Only the corresponding yaw STS transfer function showed a resonance peak around 2.3 Hz, as well. The sway and roll motion do not suggest simple passive dynamics showing similar trends in apparent mass characterised by an ever-decreasing gain and no resonance peaks. Finally, the surge, heave, pitch and yaw apparent mass transfer functions suggest that higher body mass in general yields higher peak magnitude and lower resonance frequency. This effect was most apparent for the surge motion.

Brake squeal is a well know problem in the bicycle industry. Most of the time brake squeal occurs due to wear of the brake components and/or in certain environmental conditions (e.g. rain and dirt). Bicycle brake squeal is often solved by trial and error. Compared to automotive industry, there is not much research done on the fundamental cause of bicycle brake squeal. As a result, bicycle brands are not able to guarantee that their bicycles will not produce squeal noise over time. The goal of this graduation project is to get more insight into the fundamental cause of bicycle brake squeal and to find the crucial parameters that influence bicycle brake squeal.

The track bound sliding sport of Skeleton was permanently added to Winter Olympics programme in 2002. This has led to increased interest in the sport. Engineering has already proved to be a vital contributor to improved performance in the related sport of Bobsleighing. We hope that engineering can do the same for Skeleton. This report describes an attempt at developing a platform to be used as a real-time training simulator for the sport of Skeleton. For a multitude of reasons athletes are, on average limited to a total of two hours of practice and competitive on-track time in any given year. When compared with time spent practising and in competition in most sports, this is extremely low. It is hypothesized that a simulator can augment track time by providing a realistic environment to practise in, even when access to a track is not available. This work is guided by simulators that have been developed for Bobsleighing. The main components are the models to describe the dynamics of the sport, an input method and visualization of the simulation. The main considerations for the dynamic model are of the track surface, the sled and contact between sled and track surface. These models lead to a system of equations which when solved provide accelerations and contact forces. The accelerations are integrated over a fixed time interval to determine changes in velocities, position and orientation. The position and orientation obtained after the integration is passed on to a game engine which provides the user with real-time visual output of the position and orientation along a digitally recreated track surface. A video game controller was chosen to serve as the input device. It has two joysticks, which can be mapped so as to mimic the forces applied by an athlete. A number of descents were performed using this platform both at real-time speed and at a slower speed to give the user, unfamiliar with the sport, a better chance to steer the sled. We were able to consistently reach the exit of curve 2 in real-time speed and curve 4 at the slower play speed before failure of the simulation. In most cases the algorithm used here proves to take lesser time for computation than the chosen integration time step, which is a great sign for future development as we did not make any attempts to optimise its omputation
time. We made an attempt at validation using time elapsed to traverse a certain distance and the sum of magnitude of Lagrangian multipliers. We had poor results with the time elapsed comparison, with simulated runs being 15% slower than competitive descents. While the sum of Lagrangian multipliers showed good relation to expected behaviour. This first attempt was reasonably successful, and we believe that the lessons learnt from this work has brought us one step closer to realizing a training simulator that can be useful to Skeleton athletes.

In Bicycle Motocross (BMX) racing, a fast gate start is crucial for overall race performance (Rylands2014). The goal of this study was to (1) provide a better understanding of how cyclists effectively propel the bicycle during BMX gate start, and (2) identify pedalling variables which are good predictors of a fast start."