Computer simulation of the takeoff in springboard diving

A computer simulation model of a springboard and a diver was developed to investigate diving takeoff techniques in the forward and the reverse groups. The springboard model incorporated vertical, horizontal and rotational movements based on experimental data. The diver was modelled as an eight-segment link system with torque generators acting at the metatarsal-phalangeal, ankle, knee, hip and shoulder joints. Wobbling masses were included within the trunk, thigh and shank segments to allow for soft tissue movement. The foot-springboard interface was represented by spring-dampers acting at the heel, ball and toes of the foot. The model was personalised to an elite diver so that simulation output could be compared with the diver's own performance. Kinematic data of diving performances from a one-metre springboard were obtained using high speed video and personalised inertia parameters were determined from anthropometric measurements. Joint torque was calculated using a torque / angle / angular velocity relationship based on the maximum voluntary torque measured using an isovelocity dynamometer. Visco-elastic parameters were determined using a subject-specific angledriven model which matched the simulation to the performance in an optimisation process. Four dives with minimum and maximum angular momentum in the two dive groups were chosen to obtain a common set of parameters for use in the torque-driven model. In the evaluation of the torque-driven model, there was good agreement between the simulation and performance for all four dives with a mean difference of 6.3%. The model was applied to optimise for maximum dive height for each of the four dives and to optimise for maximum rotational potential in each of the two dive groups. Optimisation results suggest that changing techniques can increase the dive height by up to 2.0 cm. It was also predicted that the diver could generate rotation almost sufficient to perform a forward three and one-half somersault tuck and a reverse two and one-half somersault tuck.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

OPTIMISATION OF TAKEOFF TECHNIQUE FOR MAXIMUM FORWARD ROTATION IN SPRINGBOARD DIVING

The aim of this study was to optimise springboard diving takeoff technique for maximum forward rotation using a computer simulation model. A planar eight-segment model of a diver with torque generators together with a springboard model was developed. The model was evaluated by comparing simulation output with an elite diver's performance. The model was then used to optimise takeoff techniques for maximum rotational potential in the forward dive group by varying the activation timings of the torque-generators. There was a 20% increase in rotational potential in the optimised simulation compared to a performance of a forward two and one-half somersault pike (105 B) dive. The results highlight the importance of technique in springboard diving since by changing only the activation timing alone the diver can generate substantially more forward rotation

A NEW MODEL OF THE SPRINGBOARD IN DIVING

This paper presents a model which describes the vertical, horizontal and rotational movement of a diving springboard. Model parameters were determined from experimental data. The springboard model was used in conjunction with a diver model to simulate a diving takeoff. Diving performance of an elite female diver was recorded at 200 Hz and was digitised to obtain kinematic data used to drive the simulation. There was good agreement in terms ot linear and angular takeoff conditions between the performance and the simulation. It is concluded that the proposed model is an improved representation of the springboard as a simple mass-spring system. This model will be used in conjunction with a diver model to investigate takeoff techniques and optimise diving performance

CONSTRAINTS AND ROBUSTNESS CONSIDERATIONS IN THE OPTIMISATION OF SPRINGBOARD DIVING TAKEOFF TECHNIQUE: A SIMULATION STUDY

The aim of this study was to investigate the effects of imposing anatomical constraints and robustness requirements on the optimisation of springboard diving takeoff technique. A planar eight-segment model of a diver with torque generators together with a springboard model was used to optimise takeoff techniques for maximum rotational potential in the forward dive group by varying the activation timings of the torque-generators. Optimisation 1 imposed no constraints or robustness requirements. Optimisation 2 imposed anatomical constraints. Optimisation 3 imposed anatomical constraints and a requirement of robustness to perturbations in activation timing. The results showed that imposing both anatomical constraints and robustness requirements have a substantial effect on optimum simulated performance

Maximising forward somersault rotation in springboard diving

Performance in the flight phase of springboard diving is limited by the amounts of linear and angular momentum generated during the takeoff phase. A planar 8-segment torquedriven simulation model combined with a springboard model was used to investigate optimum takeoff technique for maximising rotation in forward dives from the one metre springboard. Optimisations were run by varying the torque activation parameters to maximise forward rotation potential (angular momentum x flight time) while allowing for movement constraints, anatomical constraints, and execution variability. With a constraint to ensure realistic board clearance and anatomical constraints to prevent joint hyperextension, the optimised simulation produced 24% more rotation potential than a simulation matching a 2½ somersault piked dive. When 2 ms perturbations to the torque onset timings were included for the ankle, knee and hip torques within the optimisation process, the model was only able to produce 87% of the rotation potential achieved in the matching simulation. This implies that a pre-planned technique cannot produce a sufficiently good takeoff and that adjustments must be made during takeoff. When the initial onset timings of the torque generators were unperturbed and 10 ms perturbations were introduced into the torque onset timings in the board recoil phase, the optimisation produced 8% more rotation potential than the matching simulation. The optimised simulation had more hip flexion and less shoulder extension at takeoff than the matching simulation. This study illustrates the difficulty of including movement variability within performance optimisation when the movement duration is sufficiently long to allow feedback corrections

Determining effective subject-specific strength levels for forward dives using computer simulations of recorded performances

This study used optimisation procedures in conjunction with an 8-segment torque-driven computer simulation model of the takeoff phase in springboard diving to determine appropriate subjectspecific strength parameters for use in the simulation of forward dives. Kinematic data were obtained using high-speed video recordings of performances of a forward dive pike (101B) and a forward 2½ somersault pike dive (105B) by an elite diver. Nine parameters for each torque generator were taken from dynamometer measurements on an elite gymnast. The isometric torque parameter for each torque generator was then varied together with torque activation timings until the root mean squared (RMS) percentage difference between simulation and performance in terms of joint angles, orientation, linear momentum, angular momentum, and duration of springboard contact was minimised for each of the two dives. The two sets of isometric torque parameters were combined into a single set by choosing the larger value from the two sets for each parameter. Simulations using the combined set of isometric torque parameters matched the two performances closely with RMS percentage differences of 2.6% for 101B and 3.7% for 105B. Maximising the height reached by the mass centre during the flight phase for 101B using the combined set of isometric parameters and by varying torque generator activation timings during takeoff resulted in a credible height increase of 38 mm compared to the matching simulation. It is concluded that the procedure is able to determine appropriate effective strength levels suitable for use in the optimisation of simulated forward dive performances

Parameter determination for a computer simulation model of a diver and a springboard

This study used kinematic data on springboard diving performances to estimate visco-elastic parameters of a planar model of a springboard and diver with wobbling masses in the trunk, thigh and calf segments and spring-dampers acting at the heel, ball and toe of the foot segment. A subject-specific angle-driven eightsegment model was used with an optimisation algorithm to determine visco-elastic parameter values by matching simulations to four diving performances. Using the parameters determined from the matching of a single dive in a simulation of another dive resulted in up to 31% difference between simulation and performance, indicating the danger of using too small a set of kinematic data. However using four dives in a combined matching process to obtain a common set of parameters resulted in a mean difference of 8.6%. Since these four dives included very different rotational requirements, it is anticipated that the combined parameter set can be used with other dives from these two groups

Effect of fatigue and hypohydration on gait characteristics during treadmill exercise in the heat while wearing firefighter thermal protective clothing.

This study compared the gait characteristics of individuals walking in heat while wearing firefighting equipment in fatigued and non-fatigued states. Nineteen subjects performed a 50-min treadmill protocol in a heated room while gait patterns were recorded using a digital video camcorder. Forty gait cycles were analyzed near the beginning (9 min) and at the end (39-49 min) of exercise. Spatio-temporal gait variables including step frequency, step length, swing time, stance time, cycle time and double-support time were determined. Gait variability was quantified by the standard deviation (SD) and coefficient of variation (CV) of each variable. Left-right symmetry was calculated using the symmetry index (SI) and symmetry angle (SA). Paired t-tests (alpha = 0.05) were performed to identify difference between the beginning and the end of the protocol for each measured variable. Spatio-temporal gait characteristics did not differ between the beginning and the end of exercise. Gait variability of the double-support time increased at the end as measured by both SD (P = 0.037) and CV (P = 0.030) but no change was observed for other variables. Left-right symmetry measured using either SI or SA did not differ between sessions. In summary, spatio-temporal gait characteristics and symmetry while wearing firefighting equipment are insensitive to physiological fatigue. Prolonged walking in heat while wearing firefighting equipment may increase gait variability and therefore the likelihood of a fall. Future studies are needed to confirm the potential relationship between fatigue and gait variability and to investigate the possible influence of individual variation

Comparison of centre of gravity and centre of pressure patterns in the golf swing

Analysing the centre of pressure (COP) and centre of gravity (COG) could reveal stabilising strategies used by golfers throughout the golf swing. This study identified and compared golfers’ COP and COG patterns throughout the golf swing in medial–lateral (ML) and anterior–posterior (AP) directions using principal component analysis (PCA) and examined their relationship to clubhead velocity. Three-dimensional marker trajectories were collected using Vicon motion analysis and force plate data from two Kistler force plates for 22 low-handicap golfers during drives. Golfers’ COG and COP were expressed as a percentage distance between their feet. PCA was performed on COG and COP in ML and AP directions. Relationships between principal component (PC) scores were examined using Pearson correlation and regression analysis used to examine the relationship with clubhead velocity. ML COP movements varied in magnitude (PC1), rate of change and timing (PC2 and PC3). The COP and COG PC1 scores were strongly correlated in both directions (ML: r = 0.90, P < .05; AP: r = 0.81, P < .05). Clubhead velocity, explained by three PCs (74%), related to timing and rate of change in COPML near downswing (PC2 and PC3) and timing of COGML late backswing (PC2). The relationship between COPML and COGML PC1 scores identified extremes of COP and COG patterns in golfers and could indicate a golfer’s dynamic balance. Golfers with earlier movement of COP to the front foot (PC2) and rate of change (PC3) patterns in ML COP, prior to the downswing, may be more likely to generate higher clubhead velocity

Comparison of two- and three-dimensional methods for analysis of trunk kinematic variables in the golf swing

This is the as accepted for publication version of a paper subsequently published in the Journal of Applied Biomechanics © Human Kinetics. The definitive version is available at: http://dx.doi.org/10.1123/jab.2015-0032Two-dimensional methods have been used to compute trunk kinematic variables (flexion/extension, lateral bend, axial rotation) and X-factor (difference in axial rotation between trunk and pelvis) during the golf swing. Recent X-factor studies advocated three-dimensional (3D) analysis due to the errors associated with two-dimensional (2D) methods, but this has not been investigated for all trunk kinematic variables. The purpose of this study was to compare trunk kinematic variables and X-factor calculated by 2D and 3D methods to examine how different approaches influenced their profiles during the swing. Trunk kinematic variables and X-factor were calculated for golfers from vectors projected onto the global laboratory planes and from 3D segment angles. Trunk kinematic variable profiles were similar in shape; however, there were statistically significant differences in trunk flexion (-6.5 ± 3.6°) at top of backswing and trunk right-side lateral bend (8.7 ± 2.9°) at impact. Differences between 2D and 3D X-factor (approximately 16°) could largely be explained by projection errors introduced to the 2D analysis through flexion and lateral bend of the trunk and pelvis segments. The results support the need to use a 3D method for kinematic data calculation to accurately analyze the golf swing