. 18 Although electromagnetic tracking systems are a better alternative and would be a suitable technique for assessing functional activities (eg, gait, STS) in a clinical setting, the quantitative analysis of functional activities using optical motion analysis systems is well established, and has
Mohammad Reza Pourahmadi, Ismail Ebrahimi Takamjani, Shapour Jaberzadeh, Javad Sarrafzadeh, Mohammad Ali Sanjari, Rasool Bagheri, and Morteza Taghipour
Shaun D’Auria and Tim Gabbett
The purpose of this study was to investigate the physiological demands of field players in international women’s water polo match play.
Video footage was collected at the 13th FINA Women’s Water Polo World Cup in Perth in 2002. Video recordings were analyzed using a simple hand-based notation system to record predefined activity durations, frequencies, and corresponding subjective intensities.
Average exercise bout duration was 7.4 ± 2.5 s and exercise to rest ratio within play 1:1.6 ± 0.6. The average pattern of exercise was represented by 64.0 ± 15.3% swimming, 13.1 ± 9.2% contested swimming, 14.0 ± 11.6% wrestling, and 8.9 ± 7.1% holding position. Significant differences existed between outside and center players for percentage time swimming (67.5 ± 14.0% vs 60.2 ± 13.3%, P = .002) and wrestling (9.9 ± 9.3% vs 18.4 ± 11.1%, P = .000). A significant difference was found in the number (P = .017) and duration (P = .010) of high-intensity activity (HIA) bouts performed each quarter for outside (1.8 ± 2.2 bouts, 7.0 ± 3.4 s) and center players (1.2 ± 1.5 bouts, 5.2 ± 3.4 s). Positional differences in HIA were the result of a significant difference (P = .000) in the number of maximal/near maximal swims (outside 1.2 ± 1.5 and center 0.5 ± 0.9 per quarter).
This study characterizes international women’s water polo match play as a highly intermittent activity. Swimming, particularly high intensity, has greater significance to outside players, whereas wrestling has greater significance to center players.
Gaspare Pavei, Elena Seminati, Jorge L.L. Storniolo, and Leonardo A. Peyré-Tartaruga
We compared running mechanics parameters determined from ground reaction force (GRF) measurements with estimated forces obtained from double differentiation of kinematic (K) data from motion analysis in a broad spectrum of running speeds (1.94–5.56 m⋅s–1). Data were collected through a force-instrumented treadmill and compared at different sampling frequencies (900 and 300 Hz for GRF, 300 and 100 Hz for K). Vertical force peak, shape, and impulse were similar between K methods and GRF. Contact time, flight time, and vertical stiffness (kvert) obtained from K showed the same trend as GRF with differences < 5%, whereas leg stiffness (kleg) was not correctly computed by kinematics. The results revealed that the main vertical GRF parameters can be computed by the double differentiation of the body center of mass properly calculated by motion analysis. The present model provides an alternative accessible method for determining temporal and kinetic parameters of running without an instrumented treadmill.
Ric Lovell and Grant Abt
To report the intensity distribution of Premier League soccer players’ external loads during match play, according to recognized physiological thresholds. The authors also present a case in which individualized speed thresholds changed the interpretation of time–motion data.
Eight outfield players performed an incremental treadmill test to exhaustion to determine the running speeds associated with their ventilatory thresholds. The running speeds were then used to individualize time–motion data collected in 5 competitive fixtures and compared with commonly applied arbitrary speed zones.
Of the total distance covered, 26%, 57%, and 17% were performed at low, moderate, and high intensity, respectively. Individualized time– motion data identified a 41% difference in the high-intensity distance covered between 2 players of the same positional role, whereas the player-independent approach yielded negligible (5–7%) differences in total and high-speed distances covered.
The authors recommend that individualized speed thresholds be applied to time–motion-analysis data in synergy with the traditional arbitrary approach.
Josh L. Secomb, Jeremy M. Sheppard, and Ben J. Dascombe
To provide a descriptive and quantitative time–motion analysis of surfing training with the use of global positioning system (GPS) and heart-rate (HR) technology.
Fifteen male surfing athletes (22.1 ± 3.9 y, 175.4 ± 6.4 cm, 72.5 ± 7.7 kg) performed a 2-h surfing training session, wearing both a GPS unit and an HR monitor. An individual digital video recording was taken of the entire surfing duration. Repeated-measures ANOVAs were used to determine any effects of time on the physical and physiological measures.
Participants covered 6293.2 ± 1826.1 m during the 2-h surfing training session and recorded measures of average speed, HRaverage, and HRpeak as 52.4 ± 15.2 m/min, 128 ± 13 beats/min, and 171 ± 12 beats/min, respectively. Furthermore, the relative mean times spent performing paddling, sprint paddling to catch waves, stationary, wave riding, and recovery of the surfboard were 42.6% ± 9.9%, 4.1% ± 1.2%, 52.8% ± 12.4%, 2.5% ± 1.9%, and 2.1% ± 1.7%, respectively.
The results demonstrate that a 2-h surfing training session is performed at a lower intensity than competitive heats. This is likely due to the onset of fatigue and a pacing strategy used by participants. Furthermore, surfing training sessions do not appear to appropriately condition surfers for competitive events. As a result, coaches working with surfing athletes should consider altering training sessions to incorporate repeated-effort sprint paddling to more effectively physically prepare surfers for competitive events.
Hyeonho Yu, Hans van der Mars, Peter A. Hastie, and Pamela H. Kulinna
(apps) may offer various advantages over other types of video technology (e.g., portable, editable, cost-effective, etc.). In particular, motion analysis apps may provide teachers with a new vehicle to deliver content and assist with teaching sports/games, while helping students develop skills and GP
The purpose of this study was twofold: (a) to investigate the effects of selected experimental factors on the magnitude of the object plane deformation due to refraction, and (b) to discuss their practical implications in an effort to improve the applicability of the 2-D DLT method in the underwater motion analysis. The RMS and maximum object plane reconstruction errors of various experimental conditions were computed systematically. To isolate the error due to refraction from the experimental errors, the comparator coordinates (image plane coordinates) of the control points were computed based on a theoretical refraction model rather than actual digitizing. It was concluded from a series of object plane reconstruction that among the distance and angle factors of the experimental setting in the 2-D underwater motion analysis, the camera-to-interface distance and the interface-to-control-object distance are the two major factors affecting the magnitude of the object plane deformation. The other factors revealed only minor effects. The advantages of the 2-D DLT method over the traditional multiplier method in underwater motion analysis. such as oblique projection and multiple camera setup. were further discussed. Possible ways to reduce the maximum reconstruction error were also explored.
Barbara C. Belyea, Ethan Lewis, Zachary Gabor, Jill Jackson, and Deborah L. King
Context: Lower-extremity landing mechanics have been implicated as a contributing factor in knee pain and injury, yet cost-effective and clinically accessible methods for evaluating movement mechanics are limited. The identification of valid, reliable, and readily accessible technology to assess lower-extremity alignment could be an important tool for clinicians, coaches, and strength and conditioning specialists. Objective: To examine the validity and reliability of using a handheld tablet and movement-analysis application (app) for assessing lower-extremity alignment during a drop vertical-jump task. Design: Concurrent validation. Setting: Laboratory. Participants: 22 healthy college-age subjects (11 women and 11 men, mean age 21 ± 1.4 y, mean height 1.73 ± 0.12 m, mean mass 71 ± 13 kg) with no lower-extremity pathology that prevented safe landing from a drop jump. Intervention: Subjects performed 6 drop vertical jumps that were recorded simultaneously using a 3-dimensional (3D) motion-capture system and a handheld tablet. Main Outcomes Measures: Angles on the tablet were calculated using a motion-analysis app and from the 3D motion-capture system using Visual 3D. Hip and knee angles were measured and compared between both systems. Results: Significant correlations between the tablet and 3D measures for select frontal- and sagittal-plane ranges of motion and angles at maximum knee flexion (MKF) ranged from r = .48 (P = .036) for frontal-plane knee angle at MKF to r = .77 (P < .001) for knee flexion at MKF. Conclusion: Results of this study suggest that a handheld tablet and app may be a reliable method for assessing select lower-extremity joint alignments during drop vertical jumps, but this technology should not be used to measure absolute joint angles. However, sports medicine specialists could use a handheld tablet to reliably record and evaluate lower-extremity movement patterns on the field or in the clinic.
Aki Salo and Paul N. Grimshaw
Eight trials each of 7 athletes (4 women and 3 men) were videotaped and digitized in order to investigate the variation sources and kinematic variability of video motion analysis in sprint hurdles. Mean coefficients of variation (CVs) of individuals ranged from 1.0 to 92.2% for women and from 1.2 to 209.7% for men. There were 15 and 14 variables, respectively, in which mean CVs revealed less than 5% variation. In redigitizing, CVs revealed <1.0% for 12 variables for the women's trials and 10 variables for the men's trials. These results, together with variance components (between-subjects, within-subject, and redigitizing), showed that one operator and the analysis system together produced repeatable values for most of the variables. The most repeatable variables by this combination were displacement variables. However, further data processing (e.g., differentiation) appeared to have some unwanted effects on repeatability. Regarding the athletes' skill, CVs showed that athletes can reproduce most parts of their performance within certain (reasonably low) limits.
Simon Roberts, Grant Trewartha, and Keith Stokes
To assess the validity of a digitizing time–motion-analysis method for field-based sports and compare this with a notational-analysis method using rugby-union match play.
Five calibrated video cameras were located around a rugby pitch, and 1 subject completed prescribed movements within each camera’s view. Running speeds were measured using photocell timing gates. Two experienced operators digitized video data (operator 1 on 2 occasions) to allow 2-dimensional reconstruction of the prescribed movements.
Accuracy for total distance calculated was within 2.1% of the measured distance. For intraoperator and interoperator reliability, calculated distances were within 0.5% and 0.9%, respectively. Calculated speed was within 8.0% of measured photocell speed with intraoperator and interoperator reliability of 3.4% and 6.0%, respectively. For the method comparison, two 20-minute periods of rugby match play were analyzed for 5 players using the digitizing method and a notational time–motion method. For the 20-minute periods, overall mean absolute differences between methods for percentage time spent and distances covered performing different activities were 3.5% and 198.1 ± 138.1 m, respectively. Total number of changes in activity per 20 minutes were 184 ± 24 versus 458 ± 48, and work-to-rest ratios, 10.0%:90.0% and 7.3%:92.7% for notational and digitizing methods, respectively.
The digitizing method is accurate and reliable for gaining detailed information on work profiles of field-sport participants and provides applied researchers richer data output than the conventional notational method.