Saddle height effects on pedal forces,joint mechanical work and kinematics of cyclists and triathletes

Abstract

The effects of saddle height on pedal forces and joint kinetics (e.g. mechanical work) are unclear. Therefore, we assessed the effects of saddle height on pedal forces, joint mechanical work and kinematics in 12 cyclists and 12 triathletes. Four sub-maximal 2-min cycling trials (3.4 W/kg and 90 rpm) were conducted using preferred, low and high saddle heights (±10° knee flexion at 6 o'clock crank position from the individual preferred height) and an advocated optimal saddle height (25° knee flexion at 6 o'clock crank position). Right pedal forces and lower limb kinematics were compared using effect sizes (ES). Increases in saddle height (5% of preferred height, ES=4.6) resulted in large increases in index of effectiveness (7%, ES=1.2) at the optimal compared to the preferred saddle height for cyclists. Greater knee (11–15%, ES=1.6) and smaller hip (6–8%, ES=1.7) angles were observed at the low (cyclists and triathletes) and preferred (triathletes only) saddle heights compared to high and optimal saddle heights. Smaller hip angle (5%, ES=1.0) and greater hip range of motion (9%, ES=1.0) were observed at the preferred saddle height for triathletes compared to cyclists. Changes in saddle height up to 5% of preferred saddle height for cyclists and 7% for triathletes affected hip and knee angles but not joint mechanical work. Cyclists and triathletes would opt for saddle heights <5 and <7%, respectively, within a range of their existing saddle height.     

Demand cycles and market segmentation in bicycle sharing

Consumers often display unique habitual behaviors, and knowledge of these behaviors is of great value in prediction of future demand. We investigated consumer behavior in bicycle sharing in Beijing, where demand prediction is critical for cost-effective rebalancing of bicycle locations (putting bikes where and when they will be rented) and supply (number of bicycles). We created baseline statistical demand models, borrowing methods from economics, signal processing and animal tracking to find consumption cycles of 7, 12, 24 h and 7-days. Lorenz curves of bicycle demand revealed significant stratification of consumer behavior and a long-tail of infrequent demand. To overcome the limits of traditional statistical models, we developed a deep-learning model to incorporate (1) weather and air quality, (2) time-series of demand, and (3) geographical location of demand. Customer segmentation was added at a later stage, to explore potential for improvement with customer demographics. Our final machine learning model with tuned hyperparameters yielded around 50% improvement in predictions over a discrete wavelet transform model, and 80–90% improvement in predictions over a naïve model the reflects some current industry practice. We assessed causality in the deep-learning model, finding that location and air quality had the strongest causal impact on demand. The extreme market segmentation of customer demand, and our relatively short time span of data combined to make it difficult to find sufficient data on all customers for a model fit based on segmentation. We reduced our model data to only the 10 most frequent to see whether such segmentation improves our model's predictive success. These results, though limited, suggest that customer behavior within market segments is more stable than across all customers, as was expected.     

Effects of workload and pedalling cadence on knee forces in competitive cyclists

Limited evidence showed that higher workload increases knee forces without effects from changes in pedalling cadence. This study assessed the effects of workload and cadence on patellofemoral and tibiofemoral joint forces using a new model. Right pedal force and lower limb joint kinematics were acquired for 12 competitive cyclists at two levels of workload (maximal and second ventilatory threshold) at 90 and 70 rpm of pedalling cadence. The maximal workload showed 18% larger peak patellofemoral compressive force PFC (large effect size, ES) than the second ventilatory threshold workload (90 rpm). In the meantime, the 90-rpm second ventilatory threshold was followed by a 29% smaller PFC force (large ES) than the 70-rpm condition. Normal and anterior tibiofemoral compressive forces were not largely affected by changes in workload or pedalling cadence. Compared to those of previous studies, knee forces normalized by workload were larger for patellofemoral (mean = 19 N/J; difference to other studies = 20–45%), tibiofemoral compressive (7.4 N/J; 20–572%), and tibiofemoral anterior (0.5 N/J; 60–200%) forces. Differences in model design and testing conditions (such as workload and pedalling cadence) may affect prediction of knee joint forces.     

用AT89C2051研制电助动自行车控制器

电助动自行车作为一种环保型的交通工具，已受到人们的青睐。它具有噪声小，无空气污染等绪多优点。采用不同技术方案来设计电助动自行车，以降低成本提高性价比。本文介绍三绕组无刷永磁直流电机的工作原理，超频使用AT89C2051研制速度控制器，其电原理及软硬件设计、并给予讨论。     

Static versus dynamic kinematics in cyclists: A comparison of goniometer,inclinometer and 3D motion capture

Kinematic measurements conducted during bike set-ups utilise either static or dynamic measures. There is currently limited data on reliability of static and dynamic measures nor consensus on which is the optimal method. The aim of the study was to assess the difference between static and dynamic measures of the ankle, knee, hip, shoulder and elbow. Nineteen subjects performed three separate trials for a 10-min duration at a fixed workload (70% of peak power output). Static measures were taken with a standard goniometer (GM), an inclinometer (IM) and dynamic three-dimensional motion capture (3DMC) using an eight camera motion capture system. Static and dynamic joint angles were compared over the three trials to assess repeatability of the measurements and differences between static and dynamic values. There was a positive correlation between GM and IM measures for all joints. Only the knee, shoulder and elbow were positively correlated between GM and 3DMC, and IM and 3DMC. Although all three instruments were reliable, 3D motion analysis utilised different landmarks for most joints and produced different means. Changes in knee flexion angle from static to dynamic are attributable to changes in the positioning of the foot. Controlling for this factor, the differences are negated. It was demonstrated that 3DMC is not interchangeable with GM and IM, and it is recommended that 3DMC develop independent reference values for bicycle configuration.     

A user-friendly calibration system for bicycle ergometers, home trainers and bicycle power monitoring devices

In this study, a new system for the calibration of bicycle ergometers, home trainers and bicycle power monitoring devices is described. This system contains a portable calibration rig as well as a specialised calibration software and is designed for easy and efficient use directly on-site by non-expert personnel. Key features of the calibration rig include a cradle used to implement a torque reaction measurement technique, roller casters, sliding coupling, and crowned splines to facilitate and speed up the calibration process. The maximum power uncertainty delivered by the calibration rig for a nominal power level range of 50–600 W is ±0.9%. A software to guide users through the calibration process and generate calibration charts is described. To illustrate how the calibration system is typically used, the calibration charts of two different brands of home trainers have been obtained, and the power output measurement accuracy of two bicycle power monitoring devices has been determined. Power discrepancies were noted. The results in this study reveal that the calibration system is an effective tool in characterising the behaviour of home trainers.