Validation of a Bar Linkage Model for Joint Angle Estimation during Cycling
Source abstract
Abstract
Measuring lower extremity joint angles during cycling is interesting to derive joint loading from contact forces at the pedals and to determine the cause of cycling injuries. Traditionally, joint angles are determined in a stationary setup with high-resolution cameras that track marker movement in a lab environment. Alternatively, joint angles can be estimated mathematically in-situ: the lower extremities, bicycle frame and pedal arms are presented as a 5 bar linkage system which is fully defined by the lower extremity segment lengths, seat height and pedal position. As most instrumented pedals for power measurements include pedal orientation measurements, the bar linkage system demands no special equipment to derive joint loadings from pedal loads. The aim of this study is to validate the bar linkage system for joint angle estimation in the sagittal plane during in-situ cycling. Ten subjects cycled on a stationary bike while the hip, knee and ankle angles were measured with a Vicon optoelectronic motion capture system and simultaneously calculated with the bar linkage system. The results were averaged to subject-specific and method-specific ensemble average curves in the function of the phase in the pedal cycle and compared by a correlation analysis, Bland Altman plot, and Spm1D paired T-test. The hip and knee angle estimation of the bar linkage system showed no statistically significant difference from the optoelectronic system. Moreover, the ankle showed a statistically significant difference in the last half of the recovery phase of the pedal cycle. As the difference was maximally 3°, it has no clinical significance when deriving joint loading from contact forces at the pedal.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lower Extremity Biomechanics and Pathologies · Knee injuries and reconstruction techniques · Sports injuries and prevention
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
KU Leuven Campus Technologiecampus, 9000 Gent, Belgium
KU Leuven Campus, 8200 Brugge, Belgium
KU Leuven Campus, 3590 Diepenbeek, Belgium
References (20)
Show source-provided references
1. The safety of electrically assisted bicycles compared to classic bicycles10.1016/j.aap.2014.09.010 ↗
2. An experiment on rider stability while mounting: Comparing middle-aged and elderly cyclists on pedelecs and conventional bicycles10.1016/j.aap.2017.01.004 ↗
3. A systematic review of parameters related to cycling overuse injuries or pain
4. Lower body problems and injury in cycling10.1016/j.jbmt.2005.01.007 ↗
5. Incidence and risk for traumatic and overuse injuries in top-level road cyclists10.1080/02640414.2012.687112 ↗
6. Biomechanical considerations in the pathogenesis of osteoarthritis of the knee10.1007/s00167-011-1818-0 ↗
7. Dynamic load at baseline can predict radiographic disease progression in medial compartment knee osteoarthritis10.1136/ard.61.7.617 ↗
8. On the relation between joint moments and pedalling rates at constant power in bicycling10.1016/0021-9290(86)90008-4 ↗
9. development of a low-cost measurement system to determine 3-Dimensional pedal loads during in-situ cycling10.20858/tp.2019.14.4.13 ↗
10. Sien, D., Jordi, D., Juwet, M., Shariatmadar, K., and Versteyhe, M. (2020). Design and calibration of an instrumented seat post to measure sitting loads while cycling. Sensors, 20.10.3390/s20051384 ↗
11. Three-dimensional kinematics of competitive and recreational cyclists across different workloads during cycling10.1080/17461391.2015.1135984 ↗
12. Methodological considerations for kinematic analysis of upper limbs in healthy and poststroke adults Part II: A systematic review of motion capture systems and kinematic metrics10.1080/10749357.2019.1611221 ↗
13. Cordillet, S., Bideau, N., Bideau, B., and Nicolas, G. (2019). Estimation of 3D knee joint angles during cycling using inertial sensors: Accuracy of a novel sensor-to-segment calibration procedure based on pedaling motion. Sensors, 19.10.3390/s19112474 ↗
14. An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking kalman filter10.3390/s150511222 ↗
15. A method for biomechanical analysis of bicycle pedalling10.1016/0021-9290(85)90019-3 ↗
16. Solutions for representing the whole-body centre of mass in side cutting manoeuvres based on data that is typically available for lower limb kinematics10.1016/j.gaitpost.2010.02.014 ↗
17. An evaluation of anatomical and functional knee axis definition in the context of side-cutting10.1016/j.jbiomech.2012.05.017 ↗
18. Cycling biomechanics: A literature review10.2519/jospt.1991.14.3.106 ↗
19. Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis10.1016/j.jbiomech.2015.02.051 ↗
20. Measuring agreement in method comparison studies10.1177/096228029900800204 ↗