J.K. Moore
Please Note
18 records found
1
Uncontrolled bicycles are generally unstable at low speeds. We add an automatically controlled steering motor to a consumer electric bicycle that stabilizes the riderless bicycle down to just below 4 km h−1 to assist a rider in balancing the vehicle. We hypothesize that a such a stabilized bicycle will reduce the probability of falling. To test the system's possible assistance during falls, we applied varying magnitude external handlebar perturbations to twenty-six participants who rode on a treadmill with the balance assist system both activated and deactivated. We show that the probability of recovering from a handlebar perturbation significantly increases when the balance assist is activated at a travel speed of 6 km h−1. This positive effect is most prominent at and around the individual riders’ perturbation resistance threshold. We conclude that use of a balance assist system in real world bicycling can reduce the number of falls that occur near riders’ control authority limits.
This paper introduces a novel methodology for user interface prototyping of Mixed Reality applications for a dynamic motion context, namely race cycling. During lab sessions participants prototyped information provisioning in 3D-space. Their choices reflected a trade-off between cost to visual-field real estate and personal value of elected information. Information type, purpose, representation, location, size, and colour were analysed across participants. Participants preferred similar information positioning in the two investigated scenarios (descent, ascent) but included different types of information in each scenario. Heatmap visualisations revealed six preferred visual-field segments, highlighting the amount and types of information as well as segments kept empty. Balanced mock-ups of optimal layouts for descent and ascent are presented. Besides presenting a methodology for both data collection and processing - that is generally applicable by usability researchers both within and outside sports - this study provides specific insights for designers of user interfaces in road race cycling.
The ollie is the base aerial human–board maneuver, foundational to most modern skateboarding tricks. We formulate and solve an optimal control problem of a two-dimensional simplified human model and a rigid body skateboard with the objective of maximizing the height of the ollie. Our solution simultaneously discovers realistic human-applied force trajectories and optimal board geometry. We accomplish this with a direct collocation formulation using a null seed initial guess by carefully modeling the discontinuous aspects of board–ground impact and foot–board friction. This leads to efficient and robust solutions that are 10 times more computationally efficient than prior work on similar problems. The solutions show that ollie height can increase 3% by decreasing the wheelbase and that a smaller board with a back-foot-dominated force strategy can give 12% higher ollies. Our model can be used to inform jump strategy and the effects of changes to the essential board geometry.
The paper presents measurements of the lateral force and self-aligning torque from cargo and city bicycle tyres. Based on the experimental data, we have determined the parameters for the Magic Formula model, for lateral force and self-aligning torque. We performed tests with VeTyT, an indoor test rig specific for bicycle tyres, under different vertical loads (ranging from 343 N to 526 N), camber angles (−5, 0, 5) deg and inflation pressure (from 300 kPa to 500 kPa). For each condition, we evaluated the cornering stiffness and found that it generally decreases with the increase in inflation pressure for the tour/city bicycle tyres. However, the cargo tyre tested showed an opposite trend, with an increase in the cornering stiffness as the inflation pressure increased from 300 kPa to 400 kPa.
Enhancing Motor Learning in Cycling Tasks
The Role of Model Predictive Control and Training Sequence
We evaluated the impact of Model Predictive Control (MPC) robotic-assisted versus unassisted training on motor learning of a complex bicycle steering task. Ten participants were divided into two groups, alternating between MPC-assisted and unassisted training to ride a steer-by-wire bicycle on a treadmill to collect virtual stars. At Baseline, Mid-Training, and Post-Training, motor skills were assessed by the average and standard deviation (SD) of distance to stars, while performance was measured by the mean absolute and SD of the steering rate. We found significant improvements in task skill and steering performance, with notable benefits observed in the performance of the group initially trained unassisted. Our findings suggest that starting the training unassisted could stimulate an internal focus (concentrating on one's own body movements) and intrinsic skill perception. This foundation may then form a basis for later integration of MPC assistance to refine further the gained motor skills. Such a sequential training approach may benefit motor skill acquisition of complex dynamics tasks. Further research is necessary to validate and apply these findings to enhance training methods.
This project was designed to understand the causes and mechanisms of bicycle disc brake noise and use that information to formulate and evaluate possible mitigation techniques. Brake noise was generated by a real bicycle running on a treadmill and recorded by microphone and laser vibrometer. Six independent variables, brake force, rotor thickness, front fork stiffness, weather conditions, spoke tension, and friction coefficient, were varied according to a one-quarter fractional factorial design. A finite element model of the rotor, pads, and calliper was also formulated and analysed. The results of these two methods, particularly the disc mode shapes and frequencies, suggest that doublet mode splitting and reconverging plays a role in noise generation and that changing the rotor mass or breaking its symmetry could interfere with such noise generation. Finally, of these mitigations, breaking disc symmetry proved the most fruitful, with noise magnitude reductions from 72% to 99%, depending on frequency.
Connected Traffic of Vulnerable Bicyclists and Automated Vehicles
Deep Learning Trajectory Generation for Realistic Simulated Bicycle Intersection Crossings