Optimizing Performance Through High Tech

Article. Optimizing Performance Through High Tech

Proceedings to the Track And Field World Championship, Edmonton Canada, 2001

The International Track and Field

Published on Thursday, August 9, 2001 by George Dales

The International

Track & Field

Coaches Association Proceedings of the XVth ITFCA Congress � Edmonton (Alberta), Canada � August 8-10, 2001

Edited by George G. Dales

Optimizing Performance Through Hi-Tech and

Internet Science

When they talk about their personal goals in sports, athletes usually say

they would like to do their best, meaning, reach their maximum performance ability. It is a matter of achieving their absolute limit in speed, strength, endurance and skill, and combining these elements with performance.

Different athletic performances can be likened to a spectrum.

  1. On one side of the spectrum are aesthetic events such as gymnastics, diving, and figure skating where success depends on the ability of the athlete to create movements that are pleasing to the referees.
  2. In the middle of the spectrum are the endurance activities, for which the athlete tries to maintain muscular contractions, for long periods of time, at sub maximal intensity levels.
  3. On the other side of the spectrum arc the explosive activities, such as sprinting, jumping and throwing, where the athletes try to achieve maximal coordinate power.

Biomechanical analysis allows investigation of the particular event in order to create the ideal model of performance. Analysis of the performer and subsequent comparison with the ideal model occurs to allow immediate feedback to the athlete showing the deviation from the optimum.

The hardware-software combination allows modeling the human body as a series of moving “links” upon which muscular gravitational, inertial and reaction forces are applied. The physical and mathematical model for such a system, although complex, is well defined. The system provides a means of measuring human motion based on the processing of video recordings of a subject’s performance. This technique demonstrates significant advantages since it is non-invasive. No wires, sensors or markers are needed to be attached to the subject (though markers can be used with automatic digitizing). In fact, the subject need not be aware that data are being collected. The present sys

tem is portable utilizing a 2 kg notebook computer. Cameras can be taken to the location of the activity and positioned in any convenient manner, so as not to interfere with the subject.

A typical performance analysis assessment consists of four distinct phases:

  1. data collection
  2. digitizing
  3. computation, and
  4. presentation of results.

Video recordings of an activity are made using two or more cameras, stationary or panned. In the digitizing process two methods can be used:

  1. visual digitizing, and
  2. automatic digitizing.

The Automatic process requires reflective markers to be placed on the athlete’s joint center.

After digitization, the computation phase of analysis is performed to compute the true three dimensional image space coordinates of the subject’s body joints from the two-dimensional digitized coordinates of each camera’s view. Computation is performed using a direct linear transformation or the newer and more powerful Physical Parameters Transformation (PPT), to determine the true image space locations in 3-D. PPT is the process using two or more cameras to create 3-D coordinates from the 2-D obtained from one camera.

When transformation is complete, a smoothing or filtering procedure is performed on the image coordinates:

  1. to remove small, random digitizing errors, and
  2. to compute body joint velocities and accelerations.

At the completion of smoothing, the true three-dimensional body joint displacements, velocities and accelerations have been computed on a continuous basis throughout the duration of the sequence.

At this point, optional kinetic calculations may he performed to complete the computation phase. Body joint displacements, velocities and accelerations are combined with body segment mass distribution to compute dynamic forces and moments at each of the body joints. Muscular contribution to these forces and moments can then be computed by selectively removing the inertial and gravitational kinetic components.

The presentation phase of analysis allows computed results to be viewed and rcconlcd 111.111umherofdiflercnt formats:

  1. Body position and body motion can be presented in both still frame and animated “stick-figure” formats in three dimensions.
  2. Results are reported graphically.
  3. Plots of body joints and segments, linear and angular displacements, velocities, acceleration, forces and moments can be produced in a number of format options.

The preceding discussion has illustrated the use of movement analysis to assess functional capacity. However, functional capacity can also he measured directly by resistive dynamometry devices.

  1. The system employs computerized feedback control of both resistance and movement during training exercise.
  2. The intelligent dynamometer allows the machine to dynamically adapt to the activity being performed rather than the traditional approach of modifying the activity to conform to the limitations of the machine,

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Case studies in applied biomechanics demonstrate the importance of considering the true patterns of motion in determining efficient performance. One of the most important parameters in training is the ability to allow the performer to achieve a movement pattern of resistance or the pattern of motion experienced by the user during the actual activity. Then, there exists the ability to modify the pattern by reprogramming the dynamometer. The standard isokinetic equipment cannot achieve these requirements.

The value of applying the principles of biomechanics to the assessment of functional performance has been clearly demonstrated. Movement analysis provides the means to quantify human activity and to provide insight into the meth anisms that contribute either to superior or inferior levels of performance. In addition, a technology has been presented that permits exercise and rehabilitation patterns to biomechanically duplicate the target activity as a measure of function capacity.

Portable Movement Analysis System

Basic and Optional Components

Biomechanical Analysis of Discus Throwing: 1996

Atlanta Olympic Games”

History was made at the Atlanta games by utilizing the Internet to provide Biomechanical data immediately for use at remote sits. Video cameras were utilized to record the events and the data was transmitted to computers on-site for conversion to digital format. Video clips of individual performances in various events were made available for downloading free of charge from the Ariel Dynamics web site usually within hours of the actual performances. The posted events were filmed from various perspectives utilizing numerous cameras providing data capable of yielding three dimensional biomechanical results. This rapid availability of sporting activities for study by scientist, athletes, coaches and the general public on the Internet is a history making event. It further illustrates the potential of the internet as a research tool.

The purpose of the research conducted at the 1996 Olympics in Atlanta was to expand the biomechanical applications and the interactive capabilities of the Internet to make sport performances rapidly available to everyone. The Track and Field events which were performed at the 1996 Atlanta Olympics were selected to illustrate these procedures, because they are unique in captivating an enthusiastic worldwide audience,


In the present study, the biomechanical analysis of the discus throwing was performed. Data was collected on preliminaries and final performances. Video cameras were placed in key positions, approximately 45 degrees to the plane of the path of the thrown object or of the athletic performance itself, in order to record the particular event. As few as three and as many as nine video cameras were utilized.

  1. One camera was placed to the rear of the event.
  2. A second camera was placed at the side, that is perpendicular to the first camera view, and
  3. A third camera was placed at approximately 45 degrees to the event.

All video cameras recorded at 60 fields per second. Figure 1, illustrates some of the various camera positions. These are dynamics video clips and one can control and observe the movement. For non compress full video one can log into our web site at: http://www. ariel-net.com and download the original video clips through FTP connection.

cure 1

Side View I Side View 2

Side View 3 Side View 4

Front View I Front View 2

Rear View I Rear View 2

Figure 1. Examples of Camera Angles in the Discus Event, Utilized in the Atlanta Olympics

Dimensions of known factors on the field and various other measured objects in the field of view were used for the calibration points. Because multiple cameras were utilized, the best views were selected for further analysis.

The video pictures were grabbed from each view with Intel Smart Video Recorder Plus Frame Grabber and the files were stored in Audio Video Interlace Format (AVI). This data was then uploaded, via satellite, to the Ariel Dynamics web site. The stored data was available to all free of charge. The AVI files can be downloaded frame by frame from the Ariel Dynamics FTP Site for digitizing. In order to conserve bandwidth, the files are in compressed video format. The resolution of compressed files are lower than the regular files but the data was able to be rapidly available which was the purpose of the study.

To download these files:

  1. Visit http://www.arielnet.com
  2. Click on the FTP Site Button
  3. Select the Olympics directory and click on the desired AVI file(s)
  4. For a detailed list and explanation of what each file contains, click the ‘Atlanta’ link from the middle frame of the main page
  5. The list is also obtainable by clicking on Matta State University, USA any of the sport icons on various pages of the site.

Videographic Calibration Procedures

Since it was impossible to enter a calibration cube into the field, other methods had to be devised. The following is a description of a unique technique that was devised to create a calibration cube from known measurement on the field and the utilization of the athlete’s body measurements. The method was checked against known official measurements of the discus circle area.

From the rear camera view, the circle diameters adjacent to each of the dividing line hash marks were digitized as control points and a scaling factor was determined using the multiplier module. Then the ends of the hash marks, circle diameter, and midline of the athlete were digitized. After conversion to real dimensions, the diameter of the circle was determined and compared to the known displacement. This measurement procedure was repeated 10 times for the top four discus performers in the Atlanta Olympics.

The data coordinate endpoints were then smoothed using a second order lowpass Butter-worth digital filter with a 10 hertz (Hz) cutoff frequency. The average error in the 250 cm diameter dimension determined for these 40 measurements was 2.88 cm (1.2%), for a subject to camera distance of over 90m. The latitudinal position of the athlete’s midline was digitized and determined using the multiplier technique. The longitudinal position of the athlete was determined using similar procedures for the side view. The average error in the circle diameter was 3.4cm (1.4%) for a distance over 90m.

Next the athlete’s standing height which was obtained from the Official Olympic Track and Field guide was entered into the calibration data with the latitudinal and longitudinal coordinates determined from the previously discussed multiplier techniques. Then using the segmental length ratios reported by Dempster (1955), the shoulder, hip and knee heights were determined for each athlete.

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These heights were used to create a 3 dimensional cube using 5 data points on the circle (left hash, left and right circle diameter, left and right sector hash) and 4 body control points.

Considering the fact that calibration objects were not allowed on the field, this method deemed to be superb. Since the variation in throws of the same athlete is more than 10 percent, the error in measurement of less than 1.5 percent was acceptable in the present study.

Resu is

The 21 data points digitized were:

  • left foot (5th metatarsal)
  • shoulder
  • ankle
  • right shoulder
  • knee
  • elbow
  • hip
  • wrist
  • right hip
  • hand
  • knee
  • discus
  • ankle
  • base of the neck
  • left wrist
  • mastoid process
  • elbow
  • top of the head
  • left and right circle diameters

at the hash marks

This composite control cube consisting of 9 points and 21 data points were digitized and entered into the 3 dimensional linear transformation (DLT) module and converted to real displacements. The real coordinate endpoints were smoothed using a 10 Hz cutoff frequency in a low-pass digital filter. The 3 dimensional displacements of the circle diameter were compared to the actual 250cm displacement. The top 4 performers’ trials yielded an average error of 2.9cm (1.2%) using the DLT transformation algorithm. This multi staged approach created a 3 dimensional cube of control points from field dimensions and human anthropo

metric measures. This made it possible to overcome the limitation of not having a pre-determined calibration cube set in the field of view and yet, obtain accurate 3dimensional track and field data from the Olympics. From the present Kinematic data, enormous amounts of results could be analyzed. However, only few parameters were selected. The parameters to be analyzed were segment velocities and Moment Arm calculations.

Figures x to y illustrates the stick figures resulted from the DLT measurements. 1. The resultant release velocities calculat

ed the best 4 throws were:

� 3080.1 cm/sec for Riedel (GER),

� 2718.5 cm/sec for Dubrovschchik


� 2599.0 cm/sec for Kaptyukh (BLR),

� 2498.0 cm/sec for Washington


2. The projection angles in the YZ plane

representing the angle in respect to the

horizontal were: 21.9, 29.1, 37.3

and 29.9 degrees for Riedel,

Dubrovschchik, Kaptyukh and Wash

ington, respectively.

3. The heights of release of the discus were:

1.5m, 1.75m, 1.6m and 1.21m for

Riedel, Dubrovschchik, Kaptyukh and

Washington, respectively.

4. The elapsed times to complete the turns

of the throw were:

� 3.0 seconds for Riedel,

� 2.3 sec for Dubrovschchik,

� 1.9 sec for Kaptyukh, and

� 1.6 seconds for Washington.

5. The combined effect of the projection velocity, projection angle and height of release resulted in medalist throws of

� 69.4m (Olympic record) by Riedel


� 66.6m by Dubrovschchik (BLR),

� 65.8m for Kaptyukh (BLR), fol

lowed by

� 65.4m for Washington (USA).

The aerodynamic variable of angle attack was not determined for these throwing trials.


The throwing velocities determined were similar to the velocities calculated for in analyses performed byAriel in 1976 on Silvester and Oerter. There were negligible differences in the projection angles used by the 4 best discus throwers in the Atlanta Olympics, but there were significant differences in the resultant projection velocities between the top 4 contestants analyzed.

Washington had a projection velocity of 2484.9cm/sec, which represented a 28% increase in solely the speed of the discus over the fourth place finisher.

Interestingly enough, Washington performed the throwing movement in 46% less time, while Riedel took the longest amount of time to release the discus. This may indicate that Washington moved across the circle too quickly, thus not allowing enough time for the storage of elastic energy in the arm during the turns and then consequently a lower enerU return was observed at the release of the discus.

The study successfully demonstrated that digitization is a biomechanical task which can be performed between different geographical locations using the Internet as the interfacing medium. The applications of this technique and intellectual resource appear unlimited.

For example, a golf teacher in New York can video his students’ swings. These video clips can be transmitted digitally in AV] format to a server in one part of the world and then interfaced to the biomechanical program for further analysis. Many Olympic events including equine events, sailing and cross-country skiing make fixed laboratory studies difficult. Coaches can film actual performances on site using cameras with direct AVI format input attached to Laptop computers.

These files can then be digitized or transmitted through Internet protocols. Biomechanical quantification has developed far beyond the pioneers who relied upon visual observations of animation to describe movement. The revolution continued with improvements in cameras, the introduction of computers, development of various algorithms to better fit the data, and expansion beyond sports studies. Additional innovations in the process are expected as the Internet further evolves into newer presentation technologies involving animation and virtual reality

(e.g., Java and VRML).

The ability to quantify motion has appeal to many groups at many different levels. Access to global resources via the Internet expands biomechanics beyond a fixed geographical location. This has direct applications in medical research and industrial engineering where, frequently, transmission and processing of research data between remote sites has to occur in a realtime mode.

Thus, the subject presented and studied in this document represents a significant threshold in furthering accessibility and applicability of Biomechanics to several scientific, medical, industrial and aeronautical endeavors far beyond its present reach.

‘Reprinted from Ariel’s Cyber Sport Quarterl)t Copyright � MCMXCVI by Giedon Ariel Lr Associates. All Rights Reserved Web Site Design by George Capili. Updated Tue. 31 Dec 96.

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