My research interests lie within both sports and clinical biomechanics applications. I rely upon merging conventional biomechanical in vivo measurements together with state-of-the-art musculoskeletal modeling and optimal control simulation approaches. The integrative approach I take enables me to understand how an individual may run faster, jump further, walk following surgery or intervention, and simultaneously estimate internal body dynamics noninvasively. As a Postdoctoral Research Scholar within the Wu Tsai Human Performance Alliance I aim to explore how stochastic optimal control and reinforcement learning methods can be applied to further our understanding of sporting performance.
Honors & Awards
Estimating spinal loads from IMUs using direct collocation - Advanced OpenSim Workshop Travel Award, Stanford University (March 2020)
Future Research Leaders - Internationalisation Funding Scheme, University of Bath and Polytechnic University of Catalonia (April 2019)
Master of Science, Loughborough University (2015)
Bachelor of Science, Manchester Metropolitan Univ (2014)
Doctor of Philosophy, University of Bath (2021)
Ph.D., University of Bath, Biomechanics (2021)
M.S., Loughborough University, Biomechanics (2015)
B.S., Manchester Metropolitan University, Exercise and Sport Science (2014)
Scott Delp, Postdoctoral Faculty Sponsor
Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study.
2022; 12 (1): 15908
The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left 'free' to change. It was found that the 'knee-free' and 'knee-hip-free' simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.
View details for DOI 10.1038/s41598-022-20023-y
View details for PubMedID 36151260
Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach
2021; 9: e10975
Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.
View details for DOI 10.7717/peerj.10975
View details for Web of Science ID 000626263700007
View details for PubMedID 33732550
View details for PubMedCentralID PMC7950206
Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers
2020; 20 (20)
Wearable sensors and motion capture technology are accepted instruments to measure spatiotemporal variables during punching performance and to study the externally observable effects of fatigue. This study aimed to develop a computational framework enabling three-dimensional inverse dynamics analysis through the tracking of punching kinematics obtained from inertial measurement units and uniplanar videography. The framework was applied to six elite male boxers performing a boxing-specific punch fatigue protocol. OpenPose was used to label left side upper-limb landmarks from which sagittal plane kinematics were computed. Custom-made inertial measurement units were embedded into the boxing gloves, and three-dimensional punch accelerations were analyzed using statistical parametric mapping to evaluate the effects of both fatigue and laterality. Tracking simulations of a sub-set of left-handed punches were formulated as optimal control problems and converted to nonlinear programming problems for solution with a trapezoid collocation method. The laterality analysis revealed the dominant side fatigued more than the non-dominant, while tracking simulations revealed shoulder abduction and elevation moments increased across the fatigue protocol. In future, such advanced simulation and analysis could be performed in ecologically valid contexts, whereby multiple inertial measurement units and video cameras might be used to model a more complete set of dynamics.
View details for DOI 10.3390/s20205749
View details for Web of Science ID 000583007900001
View details for PubMedID 33050436
View details for PubMedCentralID PMC7601017
Mechanical and morphological determinants of peak power output in elite cyclists
SCANDINAVIAN JOURNAL OF MEDICINE & SCIENCE IN SPORTS
2020; 30 (2): 227-237
Mechanical peak power output (PPO) is a determinant of performance in sprint cycling. The purpose of this study was to examine the relationship between PPO and putative physiological determinants of PPO in elite cyclists, and to compare sprint performance between elite sprint and endurance cyclists. Thirty-five elite cyclists (18 endurance; 17 sprint) performed duplicate sprint cycling laboratory tests to establish PPO and its mechanical components. Quadriceps femoris (QVOL ) and hamstring muscle volume (HAMVOL ) were assessed with MRI, vastus lateralis pennation angle (PθVL ) and fascicle length (FLVL ) were determined with ultrasound imaging, and neuromuscular activation of three muscles was assessed using EMG at PPO during sprint cycling. For the whole cohort, there was a wide variability in PPO (range 775-2025 W) with very large, positive, bivariate relationships between PPO and QVOL (r = .87), HAMVOL (r = .71), and PθVL (r = .81). Step-wise multiple regression analysis revealed that 87% of the variability in PPO between cyclists was explained by two variables QVOL (76%) and PθVL (11%). The sprint cyclists had greater PPO (+61%; P < .001 vs endurance), larger QVOL (P < .001), and BFVOL (P < .001) as well as more pennate vastus lateralis muscles (P < .001). These findings emphasize the importance of quadriceps muscle morphology for sprint cycling events.
View details for DOI 10.1111/sms.13570
View details for Web of Science ID 000494147300001
View details for PubMedID 31598998
Reliability and validity of depth camera 3D scanning to determine thigh volume
JOURNAL OF SPORTS SCIENCES
2019; 37 (1): 36-41
Gross thigh volume is a key anthropometric variable to predict sport performance and health. Currently, it is either estimated by using the frustum method, which is prone to high inter-and intra-observer error, or using medical imaging, which is expensive and time consuming. Depth camera 3D-imaging systems offer a cheap alternative to measure thigh volume but no between-session reliability or comparison to medical imaging has been made. This experiment established between-session reliability and examined agreement with magnetic resonance imaging (MRI). Forty-eight male cyclists had their thigh volume measured by the depth camera system on two occasions to establish between-session reliability. A subset of 32 participants also had lower body MRIs, through which agreement between the depth camera system and MRI was established. The results showed low between-session variability (CV = 1.7%; Absolute Typical Error = 112 cm3) when measuring thigh volume using the depth camera system. The depth camera systematically measured gross thigh volume 32.6cm3 lower than MRI. These results suggest that depth camera 3D-imaging systems are reliable tools for measuring thigh volume and show good agreement with MRI scanners, providing a cheap and time-saving alternative to medical imaging analysis.
View details for DOI 10.1080/02640414.2018.1480857
View details for Web of Science ID 000452209700007
View details for PubMedID 29851357