Dr Catherine Tucker

The aim of this study was to develop a method to quantify movement variability in the backswing and downswing phase of the golf swing and statistically assess whether there was any relationship between movement variability and outcome variability. Sixteen highly skilled golfers each performed 10 swings wearing retro-reflective markers which were tracked by a three-dimensional (3D) motion analysis system operating at 400 Hz. Ball launch conditions were captured using a launch monitor. Performance variability was calculated for each body marker based on a scalene ellipsoid volume concept which produced a score representative of the 3D variability over the 10 trials. Outcome variability was quantified as the coefficient of variation of ball velocity for the 10 trials. The statistical analysis revealed no significant correlations between performance variability for each marker trajectory and outcome variability. Performance variability in the backswing or downswing was not related to ball velocity variability. It was postulated that individual players used their own strategies in order to control their performance variability, such that it had no effect on outcome variability. © 2013 © 2013 Taylor & Francis.

The aim of this study was to assess the effect of the application of a previously validated golfer computer model on different levels of movement variability relative to a shot outcome measure: club head velocity. Movement variability was applied to the computer model on six measures sequentially throughout the body of the computer model. Four different levels of variability, 25%, 50%, 75% and 100% variability, were applied to x, y and z positional data of the aforementioned measures. Simulations were then performed with ADAMS/LifeMOD software for each level of movement variability applied to the measures in question. Club head velocity was measured during the simulation. The results suggest that movement variability application at these landmarks does not have an effect on outcome. These results potentially have implications for the coaching of the participant.

Despite an increase in the number of full-body three-dimensional computer models of the golf swing reported in the literature, many authors do not report in detail how the models are validated. Therefore, the aim of this study was to create and validate a three-dimensional full-body computer model of a golfer with a driver in terms of its kinematic output. Single-subject analysis was used whereby one elite female golfer (handicap 0) performed 16 shots with her own driver club. A 6-camera Motion Analysis infrared camera system operating at 400 Hz recorded the kinematic data of the 27 markers located on the subject and golf club. Subsequently, this data was used to drive a computer model created in ADAMS/LifeMOD software. Model construction methods closely follow that of Nesbit (2005). Additional markers were placed on the subject and were used for model validation as opposed to driving the model. In order to initiate the movement of the model, inverse and forward dynamics calculations were carried out with the imported motion data captured from one representative trial captured during experimentation. The results illustrate a high level of correlation (average r=0.949) between the kinematic data collected in experimentation and the predicted trajectory of the virtual markers of the model. Furthermore, a comparison of the difference between the simulated and actual displacements of these markers between certain key events of the golf swing indicated there were on average small differences (0.06 m between address and top of backswing and 0.06 m between top of backswing and impact) between the model simulation and the displacement recorded during experimentation. An analysis of the temporal differences of key events (i.e. swing tempo) indicated that there was little difference (0.59% difference in both backswing and downswing time between model and actual trial) in this variable between the model and the experimental trial used to drive the model. Collectively, these results indicate that this model can accurately predict the kinematic movement pattern of the subject used to drive the model. Future work will encompass kinetic validation. At present, a full-body computer model was created and validated in terms of its kinematic output; future work will utilize data derived from this model to further investigate the golf swing.

Adlington states that ‘There is no one swing for everybody but for everybody there is one swing’ [1]. Variations in the golf swings amongst high-profile touring professionals are indicative of this. With this important construct from Adlington in mind, I will discuss the value of knowledge about movement variability, the role of science in golf teaching, and what in particular should be considered within the proposed degree programme in these contexts. What follows is my view on what should be central to a Master of Science Degree in Golf Teaching/Coaching. I am coming at this from the perspective of understanding movement patterns. This opinion is formed from my own academic knowledge and personal experience of playing golf and receiving instruction from teaching professionals.

The aim of this study was to create and validate a full-body musculoskeletal model of a golfer performing a swing with their driver club. An elite female participant performed ten shots with her driver while wearing retro-reflective markers. An optical 3-D 6-camera system captured the kinematics of the markers at 400 Hz on the participant for each trial. A launch monitor device recorded the ball and club head conditions at impact. The kinematic data from one representative trial was selected to drive inverse and forward dynamics simulations of the created model. The validation results showed a very high level of agreement between experimental and simulated trajectories for selected markers (mean r = 0.966)

This report provides a comprehensive analysis of the block start and initial acceleration phase, including spatiotemporal and kinematic analysis of the first four ground contacts. An overall time to 10 metres is also provided. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides a comprehensive analysis of the block start and initial acceleration phase, including spatiotemporal and kinematic analysis of the first four ground contacts. An overall time to 10 metres is also provided. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

IAAF Rule 230.2 states that racewalkers must have no visible (to the human eye) loss of contact with the ground and that their advancing leg must be straightened from first contact with the ground until the “vertical upright position.” The aims of this study were first to analyze racewalking judges' accuracy in assessing technique and, second, to measure flight times across a range of speeds to establish when athletes were likely to lose visible contact. Twenty racewalkers were recorded in a laboratory using a panning video camera (50 Hz), a high-speed camera (100 Hz), and three force plates (1,000 Hz). Eighty-three judges of different IAAF Levels (and none) viewed the panned videos online and indicated whether each athlete was racewalking legally. Flight times shorter than 0.033 s were detected by fewer than 12.5% of judges, and thus indicated non-visible loss of contact. Flight times between 0.040 and 0.045 s were usually detected by no more than three out of eight judges. Very long flight times (≥0.060 s) were detected by nearly all judges. The results also showed that what judges generally considered straightened knees (>177°) was close to a geometrically straight line. Within this inexact definition, IAAF World Championship-standard Level III judges were most accurate, being more likely to detect anatomically bent knees and less likely to indicate bent knees when they did not occur. For the second part, the men racewalked down a 45-m indoor track at 11, 12, 13, 14, and 15 km/h in a randomized order, whereas the women's trials were at 10, 11, 12, 13, and 14 km/h. Flight times, measured using an OptoJump Next photocell system (1,000 Hz), increased for the men from 0.015 s at 11 km/h to 0.040 s at 14 km/h and 0.044 s at 15 km/h, and for the women from 0.013 s at 10 km/h to 0.041 s at 13 km/h and 0.050 s at 14 km/h. For judging by the human eye, the threshold for avoiding visible loss of contact therefore occurred for most athletes at ~14 km/h for men and 13 km/h for women.

Introduction Race walking is an Olympic event dictated by a rule that states that no visible loss of contact with the ground should occur and that the leg must be straightened from first contact with the ground until the ‘vertical upright position’ (IAAF Rule 230.2). The measurement of contact and flight times during race walking is therefore of great interest to coaches and athletes. The aim of the study was to compare the effects of changes in speed on temporal variables in elite race walking during treadmill and overground race walking. Methods Eleven male race walkers (stature: 1.77 m (± 0.06), mass: 64.4 kg (± 4.7)) and eight female race walkers (stature: 1.67 m (± 0.09), mass: 56.1 kg (± 10.3)) participated. Fifteen of the athletes had competed at the 2016 Olympic Games or 2017 World Championships. For the overground condition, the men race walked multiple times down a 45-m indoor track at 11, 12, 13, 14 and 15 km/h in a randomised order, whereas the women’s trials were at 10, 11, 12, 13 and 14 km/h. Contact and flight times were measured for each trial using five connected 1 m strips of an OptoJump Next system (1000 Hz). For the treadmill condition (conducted on a separate day), each athlete race walked on a treadmill at five speeds for 3 min each. The speeds chosen were the same as during the overground condition and were conducted in a randomised order after a 10-min warm-up and familiarisation period. Results from the OptoJump Next system were extracted using specific settings based on the number of LEDs found optimal during a reliability study; for the overground tests, this setting was 2_2, whereas for the treadmill tests it was 0_0. Results For the overground condition, the values changed as follows (contact time / flight time): men – 11 km/h: 0.327 s / 0.015 s; 12 km/h: 0.304 s / 0.025 s; 13 km/h: 0.281 s / 0.035 s; 14 km/h: 0.267 s / 0.040 s; 15 km/h: 0.251 s / 0.044 s. For women, the values changed as follows – 10 km/h: 0.331 s / 0.012 s; 11 km/h: 0.307 s / 0.022 s; 12 km/h: 0.286 s / 0.033 s; 13 km/h: 0.269 s / 0.040 s; 14 km/h: 0.248 s / 0.049 s. For the treadmill condition, the values changed as follows: men – 11 km/h: 0.313 s / 0.021 s; 12 km/h: 0.296 s / 0.029 s; 13 km/h: 0.279 s / 0.038 s; 14 km/h: 0.261 s / 0.047 s; 15 km/h: 0.247 s / 0.053 s. For women, the values changed as follows – 10 km/h: 0.319 s / 0.023 s; 11 km/h: 0.293 s / 0.036 s; 12 km/h: 0.276 s / 0.045 s; 13 km/h: 0.258 s / 0.054 s; 14 km/h: 0.245 s / 0.059 s. Discussion Although it was unsurprising that contact time decreased with increased walking speed, and that there was a concurrent increase in flight time, what was interesting was that women had higher flight times when their speeds were matched with the men’s. Women therefore need to be more careful about displaying visible loss of contact. In addition, flight times tended to be higher during treadmill race walking at the same speed as overground (and contact times lower), suggesting that treadmill training could induce non-legal technique.

The aim of this study was to analyze changes in gait variability and symmetry in distance runners. Fourteen competitive athletes ran on an instrumented treadmill for 10,000 m at speeds equivalent to 103% of their season's best time. Spatiotemporal and ground reaction force data were recorded at 1500, 3000, 5000, 7500 and 9500 m. Gait variability and inter-leg symmetry were measured using median absolute deviation (MAD) and the symmetry angle, respectively. There were no overall changes during the running bout for absolute values, symmetry angles or variability, and there were only moderate changes in variability between successive testing distances for three variables. Even with these few changes, variability was low (<4%) at all distances for all variables measured and, on average, the athletes were symmetrical for five of the seven gait variables measured. There were greater mean asymmetry values for flight time (1.1–1.4%) and for impact force (2.0–2.9%), which might have occurred because of muscle latency as the lower limb responded passively to impact during initial contact. Although most athletes were asymmetrical (>1.2%) for at least one variable, no one was asymmetrical for more than four of the seven variables measured. Being asymmetrical in a few variables is therefore not abnormal and not indicative of asymmetrical gait and given many practitioners analyze symmetry (and variability) on an individual, case-study basis, caution should be taken when assessing the need for corrective interventions.

Race walking is an Olympic event dictated by a rule that states that no visible loss of contact with the ground should occur and that the leg must be straightened from first contact with the ground until the ‘vertical upright position’ (IAAF Rule 230.2). The measurement of flight times during race walking is therefore of great interest to coaches, athletes and judges. The aim of the study was to compare different methodologies used to measure contact and flight time in race walking. Ten male race walkers (stature: 1.78 m (± 0.05), mass: 64.4 kg (± 4.9)) and seven female race walkers (stature: 1.68 m (± 0.10), mass: 56.7 kg (± 11.0)) participated. Fourteen of the athletes had competed at the 2016 Olympic Games or 2017 World Championships. The men race walked down an indoor track at 11, 12, 13, 14 and 15 km/h (measured using timing gates and in a randomised order), whereas the women’s trials were at 10, 11, 12, 13 and 14 km/h. Contact and flight times were measured for the midsection of each trial using three adjacent 900 x 600 mm Kistler force plates (1000 Hz), 5 x 1 m strips of an OptoJump Next system (1000 Hz) and a Fastec high-speed camera (500 Hz). Results from the OptoJump Next system were extracted using five settings based on the number of LEDs that needed activating (contact begins after_contact ends when), and were annotated as 0_0, 1_1, 2_2, 3_3 and 4_4. The force plate values were considered the criterion values and measurements were assessed for reliability using Intraclass Correlation Coefficients (ICC) and 95% limits of agreement (LOA: bias ± random error). For flight time, the ICCs between the force plate and OptoJump Next were 0.846 for the 0_0 condition (LOA: .011 ± .014 s), 0.901 (1_1) (LOA: .008 ± .014 s), 0.983 (2_2) (LOA: .000 ± .008 s), 0.844 (3_3) (LOA: –.011 ± .014 s), and 0.563 (4_4) (LOA: –.023 ± .018 s). The ICC between the force plate and the high-speed video for flight time was 0.975 (LOA: –.003 ± .008 s). For contact time, the ICCs between the force plate and OptoJump Next were 0.967 for the 0_0 condition (LOA: –.011 ± .011 s), 0.982 (1_1) (LOA: –.008 ± .010 s), 0.995 (2_2) (LOA: .000 ± .010 s), 0.960 (3_3) (LOA: .011 ± .016 s), and 0.874 (4_4) (LOA: .024 ± .015 s). The ICC between the force plate and the high-speed video condition for contact time was 0.991 (LOA: .004 ± .010 s). The OptoJump Next system provided results similar to those of the gold standard force plates, with the 2_2 setting the most reliable. Users of the OptoJump Next system should therefore note that adjusting the settings of the device (from 0_0, the most likely default setting) might be necessary to achieve the most accurate results. The high-speed video recordings also provided very good reliability although the time-consuming nature of video analysis means the OptoJump Next system is better suited to providing immediate results.

The aim of this study was to analyse changes in gait variability and symmetry with increasing speed in race walkers. Eighteen international athletes race walked on an instrumented treadmill at speeds of 11, 12, 13 and 14 km·h-1 in a randomised order for 3 min each. Spatiotemporal and ground reaction force data were recorded for 30 s at each speed. Gait variability was measured using median absolute deviation and inter-leg symmetry was measured using the symmetry angle. There was an overall effect of speed on all absolute values except push-off force, but symmetry and variability (except flight time) did not change with increased speed, step length and step frequency. Most athletes were asymmetrical for at least one variable, but none was asymmetrical for more than half of the variables measured. Therefore, being asymmetrical or having higher variability (<5%) in a few variables is normal. Taking all findings together, practitioners should exercise caution when deciding on the need for corrective interventions and should not be concerned that increasing gait speed could increase injury risk through changes to athletes’ asymmetry. Race walking coaches should test at competition speeds to ensure that flight times, and any variability or asymmetry, are measured appropriately.

Racewalking is an Olympic event where athletes are not permitted a visible loss of contact, with the result that competitors try to minimise flight times. The accuracy of measurements taken during testing is dependent on valid and reliable systems to determine temporal values. The aim of the study was to compare different methodologies used to measure contact and flight times in overground and treadmill racewalking. Eighteen racewalkers completed overground and instrumented treadmill trials at 5 speeds, during which flight and contact times were measured using the OptoJump Next photocell system (1000 Hz), high-speed videography (500 Hz), and force plates (1000 Hz). Results from OptoJump Next were extracted using 5 settings based on the number of light emitting diodes (LEDs) activated (GaitIn_GaitOut), and annotated as 0_0, 1_1, 2_2, 3_3 and 4_4. Regarding flight time measurements for the overground condition, the 2_2 LED setting had the best 95% confidence interval (95% CI) for Intraclass Correlation Coefficient (ICC) (0.978 – 0.988), the least bias (0.000 s), and the lowest random error (0.008 s). For the treadmill condition, the 0_0 LED setting had the best 95% CI for ICC (0.890 – 0.957), the least bias (0.004 s), and the lowest random error (0.017 s). Although high-speed videography also provided highly reliable results, the equally reliable and quicker availability of results using OptoJump Next is beneficial in laboratory-based testing. Coaches and researches are advised to alter the system’s LED settings as appropriate, and to report these settings with their findings.

The aim of this study was to analyse gait variability and symmetry in race walkers. Eighteen senior and 17 junior athletes race walked on an instrumented treadmill (for 10 km and 5 km, respectively) at speeds equivalent to 103% of season’s best time for 20 km and 10 km, respectively. Spatio-temporal and ground reaction force (GRF) data were recorded at 2.5 km, and at 4.5, 6.5 and 8.5 km for a subsection of athletes. Gait variability was measured using median absolute deviation (MAD) whereas inter-leg symmetry was measured using the symmetry angle. Both groups showed low variability for step length (< 0.9%), step frequency (< 1.1%), contact time (≤ 1.2%) and vertical peak force values (< 5%), and neither variability nor symmetry changed with distance walked. Junior athletes were more variable for both step length (P = 0.004) and loading force (P = 0.003); no differences for gait symmetry were found. Whereas there was little mean asymmetry overall, individual analyses identified asymmetry in several athletes (symmetry angle ≥ 1.2%). Importantly, asymmetrical step lengths were found in 12 athletes and could result from underlying imbalances. Coaches are advised to observe athletes on an individual basis to monitor for both variability and asymmetry.

The aim of this study was to examine spatiotemporal and joint kinematic differences between footstrike patterns in 10,000m running. Seventy-two men’s and 42 women’s footstrike patterns were analysed during laps 5, 10, 15, 20 and 25 (of 25) using 2D video recordings. Approximately 47% of men were FFS throughout the race, 30% were MFS and 24% RFS; the respective frequencies in women were approximately 30%, 38% and 32%. Overall, 83% of men and 88% of women retained their footstrike pattern throughout the race. Amongst the 53 men and 33 women with symmetrical footstrike patterns, there were no differences in speed, step length or cadence between footstrike groups in either sex. Most lower limb joint angles did not change in these athletes during the event, with few differences between footstrike patterns apart from ankle and foot angles. A greater hip-ankle distance was found in RFS than in FFS (both sexes) and in RFS than in MFS (men only), although these differences were never more than 0.03 m. Coaches should note that habitual footstrike patterns were maintained during this long-distance track race despite changes in running speed and possible fatigue, and there were few performance differences between footstrike patterns.

The aim of this study was to examine biomechanical differences between footstrike patterns in elite 10,000m racing. Video data of 53 men and 33 women were recorded in competition and used to compare spatiotemporal and joint kinematic variables between rearfoot, midfoot and forefoot strikers, and to find associations. There were no differences between footstrike patterns for speed, step length or cadence, but rearfoot strikers had longer contact times than forefoot and midfoot strikers by 0.017 and 0.014 s, respectively, and shorter flight times by 0.023 and 0.021 s, respectively. The main causes of different footstrike patterns were the ankle and foot angles at initial contact; thigh, knee and shank angles differed little. In women, longer hip-ankle “overstriding” distances were associated with faster running speeds (r = 0.58), and so were a positive contributor to performance.

Hip-shoulder separation (H-Ssep) has been widely researched in many sporting activities (e.g., golf) to provide information on the contribution of torso rotation to performance and injury. Although it is necessary for high jumpers to generate significant long-axis rotation to successfully clear the bar, limited information exists on H-Ssep for high jump athletes. As such, this study aimed to a) characterize the H-Ssep of world-class high jump athletes during competition, b) determine if differences exist between male and female athletes and c) to examine the relationship between H-Ssep and the biomechanical parameters used to describe high jump technique. Twenty-nine world-class high jumpers (17 males, 12 females) were recorded (120-200 Hz) during the 2017 and 2018 World Athletics Championship finals. H-Ssep was quantified at touchdown (TD) and take-off (TO) following manual digitizing (SIMI motion) and a number of other common biomechanical parameters were computed. The observed levels of H-Ssep at TD (−46 ± 12o) and TO (16 ± 11o) were in line with those reported previously for other sports. The magnitude of H-Ssep varied between individuals and showed significant associations with other approach and take-off characteristics. Significant differences in H-Ssep were not evident between male and female athletes despite significant differences in other performance- and technique-related parameters. These findings highlight the divergent take-off characteristics of world-class performers and their reliance on hip-shoulder interactions when generating long axis rotation. Coaches should be mindful of the mechanical and physical consequences of H-Ssep when developing technical models, conditioning interventions and coaching strategies.

Midfoot- (MFS) and forefoot-striking (FFS) runners usually switch to rearfoot-striking (RFS) during marathons. However, world-class runners might resist modifications during shorter races. The purpose of this study was to analyse footstrike patterns, ground contact times and running speeds in a World Championship men’s 10,000 m final. Footstrike patterns and contact times of the top 12 finishing men (24 ± 5 years) were recorded (150 Hz) during laps 1, 5, 11, 15, 20 and 25. Split times for each 100-m segment were obtained. No RFS patterns were observed; there was no difference between the number of FFS and MFS athletes at any distance (p ≥ 0.581) and no change in the proportions of FFS and MFS occurred (p = 0.383). No link between race performance and footstrike pattern appeared given the similar number who used FFS or MFS and their similar finishing times. Despite slower running speeds and longer contact times in the middle of the race (p ≤ 0.024), no effect on footstrike patterns occurred. The prevalence of anterior footstrike patterns in this world-class race reflects the capability of maintaining fast paces (>22 km/h). Changes in footstrike pattern might accompany the physiological and neuromuscular effects of fatigue over longer distances.

The aims of this project were to conduct scientific research on race walking with regard to judging, and through the findings in this report to provide educational support to the IAAF for race walk judges, coaches and athletes. The study was conducted in two parts: the first involved inviting race walkers from around the world to be analysed in the biomechanics laboratories at Leeds Beckett University; the second part involved sharing the videos of the athletes with judges around the world who assessed the athletes for their adherence to IAAF Rule 230.2.

Race walking is an event where the knee must be straightened from first contact with the ground until midstance. The aim of this study was to compare knee angle measurements between 2D videography and 3D optoelectronic systems. Passive retroreflective markers were placed on the right leg of 12 race walkers and 3D marker coordinate data captured (250 Hz), with 2D video data (100 Hz) recorded simultaneously. Knee angle data were first derived based on the markers’ coordinates, and separately by using a 3D model that also incorporated thigh and shank clusters; the video data were analysed using both automatic tracking and manual digitising, creating four conditions overall. Differences were calculated between conditions for stance (using root mean square values), and at discrete events. There were few differences between systems, although the 3D model produced larger angles at midstance than using automatic tracking and marker coordinates (by 3 – 6°, P < 0.05). These differences might have occurred because of how the 3D model locates the hip joint, and because of the addition of marker clusters. 2D videography gave similar results to the 3D model when using manual digitising, as it allowed for errors caused by skin movement to be corrected.

This report provides split time analysis and key kinematic parameters for the women's 100 m finalists. Kinematic data are provided for steps during high velocity running and in the final metres of the race. Split time analysis is also provided for all three semi-finals. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides split time analysis and key kinematic parameters for the men's 100 m finalists. Kinematic data are provided for steps during high velocity running and in the final metres of the race. Split time analysis is also provided for all three semi-finals. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

The aim of this study was to analyze the link between the upper and lower body during racewalking. Fifteen male and 16 female racewalkers were recorded in a laboratory as they racewalked at speeds equivalent to their 20-km personal records [men: 1:23:12 (±2:45); women: 1:34:18 (±5:15)]; a single representative trial was chosen from each athlete for analysis and averaged data analyzed. Spatial variables (e.g., stride length) were normalized to stature and referred to as ratios. None of the peak upper body joint angles were associated with speed (p < 0.05) and there were no correlations between pelvic motion and speed, but a medium relationship was observed between peak pelvic external rotation (right pelvis rotated backwards) and stride length ratio (r = 0.37). Greater peak shoulder extension was associated with lower stride frequencies (r = −0.47) and longer swing times (r = 0.41), whereas peak elbow flexion had medium associations with flight time (r = −0.44). Latissimus dorsi was the most active muscle at toe-off during peak shoulder flexion; by contrast, pectoralis major increased in activity just before initial contact, concurrent with peak shoulder extension. Consistent but relatively low rectus abdominis and external oblique activation was present throughout the stride, but increased in preparation for initial contact during late swing. The movements of the pelvic girdle were important for optimizing spatiotemporal variables, showing that this exaggerated movement allows for greater stride lengths. Racewalkers should note however that a larger range of shoulder swing movements was found to be associated with lower stride frequency, and smaller elbow angles with increased flight time, which could be indicative of faster walking but can also lead to visible loss of contact. Coaches should remember that racewalking is an endurance event and development of resistance to fatigue might be more important than strength development.

Race walking is an event dictated by a rule that states that no visible loss of contact with the ground should occur and that the leg must be straightened from first contact with the ground until the ‘vertical upright position’ (IAAF Rule 230.2). During competition, compliance with the rule is assessed subjectively by judges but during biomechanical testing it is important to measure the knee angle objectively and accurately. The aim of this study was to compare the measurement of knee angles between 2D video and 3D optoelectronic systems during race walking. Seven elite male race walkers (stature: 1.77 m (± 0.03), mass: 65.7 kg (± 6.2)) and six elite female race walkers (stature: 1.66 m (± 0.08), mass: 58.6 kg (± 9.1)) participated in the study; in total, eight had competed at the Olympic Games. 2D video data were collected at 100 Hz using a high-speed camera. A 12-camera 3D optoelectronic motion capture system (Qualisys) operating at 250 Hz simultaneously captured the motion of three lower leg markers. The video files were digitised in two ways: first, through manually digitising by a single experienced operator; and second, using SIMI Motion’s automatic tracking function to track the three retroreflective markers. The optoelectronic files were processed through Qualisys Track Manager. All sets of knee angle data were filtered using residual analysis and interpolated to 101 points using a cubic spline. The root mean square difference (RMSD) between conditions was calculated for each individual, averaged across their five trials, and then averaged across all participants. The RMSD between the two visual digitising methods for one gait cycle was 3° (± 1). The RMSD between manual digitising and Qualisys was 4° (± 1), whereas between automatic tracking and Qualisys it was 2° (± 1). At initial contact, the mean angle calculated using manual digitising was 181° (± 2), using automatic tracking 180° (± 4), and using Qualisys 180° (± 4). The maximum angle during midstance was 185° (± 4) using manual digitising, 183° (± 5) using tracking, and 183° (± 5) using Qualisys. The minimum angle during midswing was 100° (± 6) using manual digitising, 102° (± 5) using tracking, and 101° (± 5) using Qualisys. Overall, all three methodologies gave similar results with no difference greater than 2° at any discrete gait event. It was unsurprising that the automatic tracking function in SIMI Motion and Qualisys produced similar knee angles given they used the same three joint markers, and showed that any movement of the athletes’ lower limbs out of the sagittal plane had little effect on joint angle calculation. In practical terms, using a markerless, 2D video system gave similar results to using a 3D optoelectronic system, meaning that it is appropriate for analysing in competition.

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The aim of this study was to provide a fully comprehensive laboratory-based analysis of overground elite race walking biomechanics, and evaluate differences between race walking, normal walking and running, between race walking speeds, between athlete performance standards and between sexes using advanced biomechanical data collection techniques and progressive statistical analysis methods. Forty-five international race walkers of 16 different nationalities volunteered to participate in this cross-sectional design study. The sample of athletes included two IAAF World Championship medallists, a European Champion, a European Championship silver medallist, a World U20 (junior) Champion, a Commonwealth Games silver medallist, and 18 other athletes who had competed at the Olympic Games (or had the qualifying time), IAAF World Championships, World U20 Championships or World University Games. A bespoke 72-marker marker set was used to measure whole-body kinematics. The optoelectronic system was synchronised with surface electromyography data to measure muscle activity of the upper body. The experimental trials consisted of; normal walking, race walking at 10 km PB pace, running at race walking 10 km PB best, race walking at 20 km PB pace, and race walking at training pace. Statistical parametric mapping was used to consider whole-body kinematic differences throughout the gait cycle. The pectoralis major and middle deltoid muscles separated race walking from the other gait types and was found to be important with changes in race walking speed and standard of race walker. Rectus abdominis was also found to be of importance for race walking. Sagittal plane shoulder and elbow kinematics and frontal plane thorax motion was also found to change with faster race walking speed and standard of race walker. The studies also found that race walking is a unique gait that is neither closer to normal walking nor running. This is predominantly because the knee motion is restricted by World Athletics Rule 54.2, and athletes must compensate for the lack of knee flexion during stance, which normally is one of the six determinants of walking gait, and a feature of passive shock absorption in running. Knee kinematics did not change between speeds, sex and standards. This study was the first to measure upper body muscle activation and whole body 3D kinematic data analysis of elite race walkers that considers differences between gait forms using a repeated measures design on world-class race walkers. Furthermore, the statistical approach is novel to the analysis of race walking kinematics. This information offers a much needed original contribution to the body of work conducted to date on race walking biomechanics, which could positively impact coaches and athletes in enhancing performance.

IN THIS ISSUE: Message from the President, ISBS 2015 Post Conference Report, Student Mini Research Grant Reports, ISBS Awards 2015, Call for ISBS Awards 2016, Report of Student Mentoring Program, Short Communications, ISBS Sponsors, Introducing Biomch-V, C-Motion Group Meeting

This report provides detailed split time analysis of the women's 400 m final and semi-finals. Additionally, this reports shows key spatiotemporal and kinematic step parameters of each finalist a specific stage of the race. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides detailed split time analysis of the men's 400 m final and semi-finals. Additionally, this reports shows key spatiotemporal and kinematic step parameters of each finalist a specific stage of the race. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides detailed split time analysis of the women's 200 m final and semi-finals. Additionally, this reports shows key spatiotemporal and kinematic step parameters of each finalist a specific stage of the race. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides detailed split time analysis of the men's 200 m final and semi-finals. Additionally, this reports shows key spatiotemporal and kinematic step parameters of each finalist a specific stage of the race. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This study tested the performance of OpenPose on footage collected by two cameras at 200 Hz from a real-life competitive setting by comparing it with manually analyzed data in SIMI motion. The same take-off recording from the men's Long Jump finals at the 2017 World Athletics Championships was used for both approaches (markerless and manual) to reconstruct the 3D coordinates from each of the camera's 2D coordinates. Joint angle and Centre of Mass (COM) variables during the final step and take-off phase of the jump were determined. Coefficients of Multiple Determinations (CMD) for joint angle waveforms showed large variation between athletes with the knee angle values typically being higher (take-off leg: 0.727 ± 0.242; swing leg: 0.729 ± 0.190) than those for hip (take-off leg: 0.388 ± 0.193; swing leg: 0.370 ± 0.227) and ankle angle (take-off leg: 0.247 ± 0.172; swing leg: 0.155 ± 0.228). COM data also showed considerable variation between athletes and parameters, with position (0.600 ± 0.322) and projection angle (0.658 ± 0.273) waveforms generally showing better agreement than COM velocity (0.217 ± 0.241). Agreement for discrete data was generally poor with high random error for joint kinematics and COM parameters at take-off and an average ICC across variables of 0.17. The poor agreement statistics and a range of unrealistic values returned by the pose estimation underline that OpenPose is not suitable for in-competition performance analysis in events such as the long jump, something that manual analysis still achieves with high levels of accuracy and reliability.

The effect of the inclusion of a high hurdle 13.72 m after the start line on elite sprint start and initial acceleration technique has yet to be investigated or understood. This highly novel study addresses that lack of information in an exceptional manner, through detailed biomechanical analysis of the world's best sprint and hurdle athletes, with data collected in situ at the 2018 IAAF World Indoor Championships, held in Birmingham, UK. High speed videos (150 Hz) were compared for eight sprinters and seven hurdlers for the start and initial acceleration phase of the finals of the men's 60 m and 60 m hurdles. Temporal and kinematic data were supplemented by vector coding analysis to investigate mechanisms by which these world-class athletes translate their centres of mass (CM) up to the fourth touchdown post-block exit. The sprinters and hurdlers coordinated their lower limb and trunk movement in a similar manner throughout the start and initial acceleration phases, which contributes new conceptual understanding of the mechanisms that underpin start and initial acceleration performance. Differences between groups were initiated from block set-up, with the hurdlers utilising a larger block spacing, but with the front block nearer to the start line than sprinters. Even after accounting for stature, the biggest differences in the raising of the CM occurred during the block phase, with hurdlers greater than sprinters (difference in vertical CM displacement scaled to stature = −0.037, very large effect size). Subsequent flight phases showed the biggest differences in the translation of the CM, in part due to longer flight times in the hurdlers, whilst the techniques of the two groups generally converged during the ground contact phases of initial acceleration. In highlighting that similar techniques are used by world-class sprinters and hurdlers, despite differing task constraints, this study has provided invaluable insights for scientists, coaches, and athletes, that will inform further developments in understanding and practice across both sprints and hurdles.

This report provides an analysis of all finalists’ best jumps and comprised a biomechanical analysis of the two steps before the take-off board along with the hop, step and jump sections also being presented. Finally, an analysis of the landing is also presented. This is a useful analysis for those interested in triple jump performance. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides a biomechanical analysis of the best performances of all men's long jump finalists of the three steps before take-off, with a more in-depth examination of the last step. This is a useful tool for those interested in the approach to to the take-off board. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

Information on how race walkers modulate lower body kinematics with speed is of interest to coaches for developing informed training strategies for elite athletes. Seven male Olympic race walkers volunteered to participate in the study. Twelve optoelectronic cameras (Oqus 7, Qualisys) operating at 250 Hz collected kinematic data as participants race walked at 3 different speeds down the 40 m walkway. Statistical parametric mapping (spm1d.org) was used to compare lower body kinematics in Matlab (R2016b, The Mathworks Inc.). Greater hip flexion (4°) was observed at 80-92% of the gait cycle in the 10 km trials than the training pace trials (p < 0.001, medium effect (0.65)). A more flexed hip during terminal swing in the 10 km trials might be indicative of the 0.08 m increase in step length that was present with increases in race walking speed. At the knee, greater flexion (3°) occurred during the 10 km trials than the training pace trials at 68-73% (p < 0.001, medium effect (0.5)). This study suggests that elite race walkers modulate lower body kinematics by increasing range of motion of the hip and knee as speed increases. Coaches and athletes should consider an individualised approach to this kinematic strategy with respect to the rules of race walking.

This report provides a biomechanical analysis of the best performances of all women's long jump finalists of the three steps before take-off, with a more in-depth examination of the last step. This is a useful tool for those interested in the approach to to the take-off board. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

This report provides an analysis of all finalists’ best jumps and comprised a biomechanical analysis of the two steps before the take-off board along with the hop, step and jump sections also being presented. Finally, an analysis of the landing is also presented. This is a useful analysis for those interested in triple jump performance. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research

Previous research that has identified sex-based differences in race walking gait has only considered joint positions at discrete time points such as initial contact and toe-off, potentially missing important data that occur between these gait cycle events. Therefore, the aim of this study was to compare full body kinematic waveforms of race walking gait between elite male and female race walkers. With institutional ethics approval, 15 male race walkers (mean age: 26 ± 5 years; stature: 1.78 ± 0.04 m; body mass: 64.7 ± 4.9 kg), and fifteen female race walkers (mean age: 28 ± 6 years; stature: 1.65 ± 0.08 m; body mass: 54.1 kg ± 8.4 kg) volunteered to participate in the study. Participants race walked down a 40 m walkway at speeds relative to their 10 km personal best. Twelve optoelectronic cameras (Oqus7, Qualisys) operating at 250 Hz recorded three-dimensional kinematic data from 64 retroreflective markers. Kinematic data were processed (QTM 2.17, Qualisys), time-normalised and filtered (Visual3D v5, C-motion). Statistical parametric mapping (spm1d.org) independent samples t-tests were computed for comparisons in Matlab (R2016b, The Mathworks Inc.) with an alpha level of 5%. Overall, there were very few kinematic differences between male and female race walkers. Women had more thorax rotation: just after (0-9%, P = 0.015) and before (92-100%, P = 0.015) initial contact they were more externally rotated than men. During late stance and early swing, women were more internally rotated (56-13%, P < 0.001). Women also had greater internal pelvic rotation after initial contact (~3-5%, P = 0.033), and hip internal rotation during stance (18-26%, P < 0.001). Women’s knees extended more just before toe-off (46%, P = 0.021), and flexed less during swing (61-63%, P = 0.033). Finally, women also had greater ankle dorsiflexion immediately after (0-3%, P = 0.020) and immediately before (95-100%, P = 0.010) initial contact. The greater thorax rotation could be explained by women’s smaller upper body segments, which require greater rotation to compensate for smaller moments of inertia. Greater dorsiflexion around initial contact is thought to enhance step length by increasing the effective leg length by projecting the heel forwards (Murray et al., 1983. The American Journal of Sports Medicine, 11, 68-74), and could be a compensatory mechanism for shorter leg lengths. Despite largely similar race walking gait patterns, coaches should be mindful of the subtle differences between elite male and female race walking kinematics.

We do not yet understand the concurrent validity of markerless motion capture (MMC) to measure kinematic differences between multiple gait speeds. This study determined the capacity of Theia3D (Theia Markerless Inc.) MMC to detect sagittal-plane kinematic responses to different gait speeds during walking (3 and 5 km/h) and running (10 and 12 km/h). Fourteen participants ambulated on a motorised treadmill, while markerbased motion capture, through optoelectronic cameras (Oqus 7+, Qualisys AB), and MMC, through videos (Miqus, Qualisys AB) were synchronously collected. Sagittal plane changes in pelvis, hip, knee, and ankle kinematics were compared. Mostly excellent waveform similarity was found for joint kinematic changes (coefficient of multiple determination [CMD] ≥ 0.87), but pelvic tilt was less similar (CMD ≤ 0.48). Agreement between outcome measures (joint minima and maxima, range of motion) was mostly good-to-excellent (intraclass correlation coefficient [ICC] = 0.475-0.950) with standard error of measurement values of less than 1°. Pelvis kinematics showed lower agreement between systems (ICC = 0.032-0.776). In this study, Theia3D detected changes in hip, knee, and ankle sagittal-plane joint kinematics between speeds with a similar accuracy to the marker-based approach. Therefore, Theia3D is appropriate for use if interested in lower-limb sagittal joint kinematics, but not pelvic tilt.

English Premier League soccer players run at multiple speeds throughout a game. The aim of this study was to assess how well the duty factor, a dimensionless ratio based on temporal variables, described running styles in professional soccer players. A total of 25 players ran on an instrumented treadmill at 12, 16, and 20 km/h. Spatiotemporal and ground reaction force data were recorded for 30 s at each speed; video data (500 Hz) were collected to determine footstrike patterns. In addition to correlation analysis amongst the 25 players, two groups (both N = 9) of high and low duty factors were compared. The duty factor was negatively correlated with peak vertical force, center of mass (CM) vertical displacement, and leg stiffness (kleg) at all speeds (r ≥ −0.51, p ≤ 0.009). The low duty factor group had shorter contact times, longer flight times, higher peak vertical forces, greater CM vertical displacement, and higher kleg (p < 0.01). Among the high DF group players, eight were rearfoot strikers at all speeds, compared with three in the low group. The duty factor is an effective measure for categorizing soccer players as being on a continuum from terrestrial (high duty factor) to aerial (low duty factor) running styles, which we metaphorically refer to as “grizzlies” and “gazelles,” respectively. Because the duty factor distinguishes running style, there are implications for the training regimens of grizzlies and gazelles in soccer, and exercises to improve performance should be developed based on the biomechanical advantages of each spontaneous running style.

INTRODUCTION: Distance runners train at different speeds to enhance their physiological and biomechanical capabilities to ensure that their aerobic and anaerobic energy systems are optimised for the demands of racing. The kinematic, kinetic, spatiotemporal, and global stiffness changes that occur as an athlete increases speed are not well understood in well-trained middle-distance runners. The aim of this study was to analyse the biomechanical responses of middle-distance athletes to increases in treadmill speed. METHODS: Thirteen male athletes (1.79 ± 0.07 m, 66.7 ± 6.1 kg, 22.3 ± 3.2 y) and two female athletes (stature: 1.69 ± 0.01 m, mass: 55.7 ± 0.4 kg, 30.9 ± 2.6 y) participated. Their mean World Athletics points for personal best performances were 1114 (± 73). Each athlete ran on a Gaitway 3D instrumented treadmill (1000 Hz) during an incremental test at 12, 16, 20 and 24 km/h. Data were collected during the second half of each 1-min stage. Two Fastec T5 high-speed cameras (200 Hz) were placed to the sides of the treadmill to record each side of the body separately, and the starting times were synchronised with the treadmill’s data collection period. Ground reaction force (GRF) and spatiotemporal data were measured using the treadmill software; lower limb joint angles were measured using the high-speed videos in SIMI Motion; and global stiffness characteristics were calculated using peak vertical GRF via the methods of Morin et al. (JAB, 2005, 21(2), 167–180). RESULTS: Both step length and cadence increased at each faster running speed (from 1.24 m and 2.70 Hz at 12 km/h to 2.01 m and 3.32 Hz at 24 km/h). Ground contact time decreased during each stage (0.229, 0.194, 0.168 and 0.147 s, respectively), but flight time only increased until 20 km/h (0.143, 0.160 and 0.163 s), with lower values at 24 km/h (0.155 s). Duty factor decreased during each stage (0.308, 0.274, 0.254 and 0.244, respectively) although leg stiffness was consistent throughout testing (11.4, 11.4, 11.4 and 11.6 N/mm, respectively). Vertical push-off rate increased consistently during each stage (31.6, 41.4, 51.2 and 59.9 BW/s, respectively). The main changes that occurred in joint angles and positions at initial contact were an increase in thigh angle (21, 25, 28 and 29°, respectively), shank angle (3, 5, 7 and 8°, respectively) and hip-ankle horizontal distance (0.18, 0.22, 0.25 and 0.27 m, respectively). CONCLUSION: It was unsurprising that athletes increased step length and cadence with faster treadmill belt speeds, although the increase in cadence from 20 to 24 km/h was the only one that arose from both shorter contact and flight times. The lack of reliance on increased flight time, and the very small increases in joint angles and positions from 20 to 24 km/h, show that there is an anthropometric limit on achieving faster speeds, which require greater force production during the push-off phase. Athletes should thus note the need for appropriate strength and conditioning within their training regimens.

The aim was to investigate the kinematic factors associated with successful performance in the initial acceleration phase of a sprint in the best male athletes in the World at the 2018 World Indoor Athletics Championships. High speed video (150 Hz) was captured for eight sprinters in the men's 60 m final. Spatio-temporal and joint kinematic variables were calculated from the set position to the end of the first ground contact post-block exit (GC1). Normalised average horizontal external power (NAHEP) defined performance and was the dependent variable for a series of regression analyses. Clear relationships were found between GC1 NAHEP and 10-m time, 60-m time, change in velocity, acceleration and contact time in the first ground contact (r = –0.74, –0.64, 0.96, 0.91 and –0.56, respectively). Stepwise multiple linear regression of joint kinematic variables in the first ground contact revealed that trunk angle at take-off and thigh separation angle at take-off explained nearly 90% of variation in GC1 NAHEP (R

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We evaluated sprint mechanical asymmetry in world-class competitors and evaluate whether interlimb sex-based differences in sprinting mechanics exist. The eight finalists in the men’s and women’s 100 m events at the 2017 IAAF World Championships were studied. Five high-speed cameras (150 Hz) were used to capture two consecutive steps of the whole body between 47.0 m and 55.5 m from the start, while four additional cameras (250 Hz) focussed on the lower extremities. A total of 33 spatio-temporal, touchdown and toe-off joint angles, and horizontal and vertical foot velocity parameters were extracted through three-dimensional analysis. Group mean asymmetry scores were assessed using the symmetry angle (SA) where scores of 0% and 100% represent perfect symmetry and perfect asymmetry, respectively. Although considered generally low (SA < 3% for 22 out of 33 parameters), the magnitude of mechanical asymmetry varied widely between sprinters of the same sex. However, there was no mean SA scores difference between men and women for any stride mechanical parameters (all P≥0.064). Asymmetry scores were inconsistent between parameters and phases (touchdown vs toe-off instants), and sprinting mechanics were generally not related to asymmetry magnitudes. In summary, low to moderate asymmetry is a natural phenomenon in elite sprinting. Asymmetry was inconsistent between parameters and competitors during near maximum-velocity running, yet mean values for a given parameter generally did not differ between sexes. Sprinters’ performances were not related to their SA scores.

INTRODUCTION: Markerless motion capture (MMC) is increasing in popularity among biomechanists because of the reduced data collection time and removal of subjects needing to wear tight, minimalist clothing [1]. However, gait analysis often requires subjects to walk or run at multiple speeds, such as in an incremental exercise test. The sensitivity of MMC to detect kinematic changes across speeds has yet to be thoroughly explored, so the aim of this study was to compare kinematic responses to changes in gait speed when measured with a widely used marker-based system versus a MMC system. METHOD: Fifteen healthy, adult participants walked on an instrumented treadmill (1,000 Hz; Gaitway3D; h/p/cosmos) at 3 and 5 km/h and ran at 10, 11, and 12 km/h. A 14-camera optoelectronic motion capture system (Oqus 7+, Qualisys) was used to collect marker data, where markers were placed according to Cappozzo et al. [2]. Markerless video data were collected synchronously with 12 high-speed video cameras (Miqus, Qualisys). Both systems were sampling at 100 Hz. Markerless data were exported to Theia3D for processing, before being exported to Visual3D for modelling alongside marker data. Gait events were determined using the kinetic data, which was the same for both motion capture systems. Kinematic data were exported to MATLAB to calculate changes in sagittal angular data between gait speeds. RESULTS: For walking (changes between 3-5 km/h), MMC demonstrated the capacity to measure similar changes in joint range of motion (ROM), peak flexion, and peak extension for hip, knee, and ankle joints (ICC[3,1] ≥ 0.892) when compared to marker-based data, and there were no significant differences between the change in joint kinematics between systems (p > 0.05). MMC also displayed moderate-to-excellent agreement for knee and ankle joint kinematics during running (changes between 10-11 and 11-12 km/h), including ROM and peak flexion/extension (ICC ≥ 0.626). However, the hip joint was less consistent, with poor-to-moderate agreement generally being found, especially in peak hip extension (ICC = 0.198 when comparing differences between 11-12 km/h). There were no significant differences between systems during running (p < 0.05). CONCLUSION: MMC was able to measure small changes in joint angles during walking at similar magnitudes to traditional marker-based motion capture, which is promising for clinical biomechanists and gait analysis clinics. However, MMC importantly performs less well when trying to measure joint angle changes during different running speeds, with varying results between lower limb joints. Researchers and practitioners should be cautious when interpreting sagittal-plane kinematic changes during running when employing MMC as the chosen method of motion capture. REFERENCES: [1] Kanko, RM et al. (2021) J Biomech;127:110665 [2] Cappozzo, A et al. (1995) Clin Biomech;10:171-8

This report provides a comprehensive split time analysis between each hurdle, as well as key kinematic parameters for the clearance of hurdle five, including take-off and touchdown postural characteristics. Temporal analysis of the step leading up to the finish line are also provided for the final. Hurdle split times are also provided for the semi-finals. The full report is available from the IAAF website: https://www.iaaf.org/about-iaaf/documents/research