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A computer vision approach to analyse and improve performance of elite athletes in javelin throw

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Javelin-throw Analysis

Uncovering the objective

The primary objective in the sport of javelin throw is to achieve the maximum distance of flight of javelin. This distance traveled by the javelin is dependent mainly on the angle of release of the javelin and the approach run to gain velocity.

The javelin throw is different from all other overhead throws as it is more of an over-arm whip and flip motion that uses the efforts of the entire body. Terms such as fling, flip, the whip are much more descriptive and result in a more relaxed sequentially efficient delivery in which the arm of the athlete gets involved in the projection only after the major muscles of the legs, hips, and trunk have been utilized.

The best javelin throw of all time was performed by Jan Zelezny, of the Czech Republic in 1966. The result was 98.48m. In the second place, comes Johannes Vetter of Germany in 2017, with a distance of 94.44m. In third place, comes Thomas Rohler, also from Germany, with a throw of 93.90m, in 2017.

The flight distance of the javelin is determined by the release parameters such as the angle of release, the height of release, and velocity of release, as well as by the forces that act on the javelin during its flight. The former is under the control of the thrower, whereas the latter is not.

The objective here is to find the optimal body posture of the athlete at each step during the approach phase for achieving maximum javelin flight. We make use of an open-source framework, called Mediapipe, introduced by Google to build a machine learning model that analyzes the body posture of the athlete at each step in the approach phase and the release phase.

Methodology

The body posture of top javelin throw athletes was measured by building a machine learning model that estimates the body posture of an athlete in each frame of videos collected from the internet. These athletes include:

  • Neeraj Chopra
  • Johannes Vetter
  • Thomas Rohler
  • Andreas Hofmann
  • Magnus Kirt

Since the information such as the number of steps taken in the approach phase varied from one video to another, all the videos are trimmed such that each of them consists of the last six steps, which is generally referred to as the crossover phase. After undergoing trimming, we remain with videos that have 90 frames each. According to studies conducted at Concordia University, NE, on analyzing the biomechanics behind javelin throw, seven main muscle groups play a major role in determining the velocity gained by the athlete in the approach phase. These muscle groups are as follows:

  • Gastrocnemius muscle
  • Soleus muscle
  • Quadriceps
  • Hamstrings
  • Rectus Abdominis
  • Triceps
  • Rhomboids

To measure the position of these muscle groups, we calculate 8 different angles from the mediapipe Pose framework. The landmarks of these angles are as shown in the image below:

Picture1

The pose-estimation model is then implemented, which iterates through all the videos to calculate the eight different angles of each frame of the video, and these angles collected are stored in separate CSV(comma separated values) files. After this process, the angles obtained from each video are analyzed to find the position of the athlete when each of the 6 steps in the crossover phase is placed and the position of the athlete during the release phase.

These angles at each step in the approach phase are stored in 7 separate CSV file which is used for further analysis. Each of the final CSV files that are used to derive the optimal position contains the following features:

  • Left hip angle
  • Right hip angle
  • Left knee angle
  • Right knee angle
  • Left trunk angle
  • Right trunk angle
  • Dominant Elbow angle
  • Dominant Shoulder angle
  • Height of athlete
  • Weight of athlete
  • Distance traveled by javelin

After this process, regression models were built for all the CSV files of the 7 steps to find the optimal body position to achieve a different range of distances.

Results

The Regression models that were built to find the optimal body position at each of the 7 steps are then used to predict the optimal position of the 7 muscle groups to achieve a different range of distances. The table below shows the different body positions to achieve a range of distances:

At step 1:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
85-87 1.8 86 100 90 151 100 163 166 155 36
1.85 91 102 102 175 121.08 154 146 167 117
1.9 95 110 105 140 90 173 176 155 40
88-90 1.8 86 105 110 175 122 144 116 162 111
1.85 91 102 105 173 121.7 134 104 152 122
1.9 95 106 111 167 141 143 107 154 112
91-93 1.8 86 103 107 176 144 153 107 127 101
1.85 91 106 112 166 143 154 104 121 86
1.9 95 120 90 171 147 156 165 134 82

At step 2:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 71 97 138 150 160 160 166 70
1.85 91 80 67 138 144 152 147 174 68
1.9 95 84 63 131 139 152 157 176 65
85-87 1.8 86 87 74 145 141 163 126 169 102
1.85 91 92 104 153 147 167 137 167 99
1.9 95 90 97 127 132 162 138 169 101
88-90 1.8 86 116 94 122 165 144 131 143 81
1.85 91 96 104 142 133 166 127 131 79
1.9 95 70 99 162 154 146 132 143 76
91-93 1.8 86 118 107 144 167 137 143 147 84
1.9 95 90 97 157 132 172 148 145 101

At step 3:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 84 103 138 161 142 159 161 79
1.85 91 86 99 132 153 147 161 160 69
1.9 95 89 97 127 149 154 163 162 67
85-87 1.8 86 96 105 157 142 173 154 157 74
1.85 91 104 98 149 147 169 167 159 86
1.9 95 102 101 152 151 170 159 155 92
88-90 1.8 86 99 104 157 131 145 162 152 102
1.85 91 101 102 149 137 160 169 149 87
1.9 95 110 103 142 139 163 157 152 96
91-93 1.8 86 114 106 157 138 125 177 152 57
1.9 95 116 112 152 134 132 159 153 61

At step 4:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 94 92 138 139 150 159 174 52
1.85 91 104 122 131 145 152 149 174 61
1.9 95 99 110 134 143 149 153 173 72
85-87 1.8 86 97 139 141 148 132 154 169 102
1.85 91 101 123 147 142 150 152 172 94
1.9 95 92 132 151 138 152 147 171 104
88-90 1.8 86 97 111 132 147 153 147 163 79
1.85 91 100 141 166 143 118 168 162 72
1.9 95 110 121 149 136 144 151 159 92
91-93 1.8 86 104 121 139 151 169 148 144 101
1.9 95 103 114 161 149 175 154 159 104

At step 5:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 115 107 163 129 164 166 161 72
1.85 91 111 114 147 149 159 156 172 69
1.9 95 99 121 161 139 163 163 167 55
85-87 1.8 86 97 114 164 145 161 150 161 75
1.85 91 92 106 156 152 159 157 159 72
1.9 95 104 99 159 147 152 157 162 77
88-90 1.8 86 113 100 162 157 149 149 151 76
1.85 91 99 105 146 161 149 152 153 69
1.9 95 104 103 151 159 150 152 151 71
91-93 1.8 86 99 95 151 167 148 145 141 66
1.9 95 97 99 147 165 150 151 146 72

At step 6 (final step):

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 91 121 163 160 149 169 139 82
1.85 91 84 119 163 165 154 172 132 110
1.9 95 80 152 164 152 136 177 122 143
85-87 1.8 86 76 113 164 170 162 164 136 111
1.85 91 73 104 161 168 162 171 140 96
1.9 95 69 132 159 173 153 167 134 76
88-90 1.8 86 76 97 169 171 166 164 121 109
1.85 91 74 93 165 167 161 164 132 156
1.9 95 77 121 162 176 169 174 142 121
91-93 1.8 86 77 92 169 172 168 164 152 72
1.9 95 72 86 167 169 154 167 143 99

During release:

Distance (m) Height (m) Weight (kg) Right Hip (°) Left Hip (°) Right knee (°) Left knee (°) Left trunk (°) Right trunk (°) Dominant Elbow (°) Dominant Shoulder (°)
82-84 1.8 86 75 127 142 176 133 159 165 164
1.85 91 77 125 156 167 152 164 166 172
1.9 95 79 112 144 172 163 168 160 161
85-87 1.8 86 67 142 167 162 154 166 164 169
1.85 91 76 119 149 170 164 170 165 164
1.9 95 74 121 139 163 166 168 159 170
88-90 1.8 86 66 100 164 174 162 169 168 152
91-93 1.8 86 76 131 142 173 169 163 162 159
96 1.8 86 104 125 132 172 177 164 165 177

Data Visualization

Approach Phase Analysis (Steps 1-6)

Step 1 Step 2
Step 1 Step 2
Step 3 Step 4
Step 3 Step 4
Step 5 Step 6
Step 5 Step 6

Release Phase

Release

Analysis and Interpretations

Overview

This section provides a comprehensive analysis of the kinematic patterns observed in the collected data, examining the angular measurements of eight key joint positions across the final six steps and release phase. The analysis reveals distinctive technical patterns that characterize successful throws and highlights critical movement sequences.

1. Approach Phase Analysis (Steps 1-6)

1.1 Lower Body Mechanics

  • Hip Angles

    • Dominant side of hip shows progressive reduction (90-70°) approaching release
    • Non-dominant side of hip maintains stability (100-120°) through approach
    • Greater hip angle differential in successful throws (85-90m)
  • Knee Angles

    • Dominant knee maintains extension (140-175°) through approach
    • Non-dominant knee shows consistent high angles (160-170°) in final phases
    • Critical stabilization in Steps 5-6

1.2 Upper Body Mechanics

  • Trunk Position

    • Non-dominant side of trunk angle: 140-160° maintained through approach
    • Dominant side of trunk shows progressive increase (140-170°) until Step 6
    • Trunk coordination peaks at release phase
  • Shoulder and Elbow Position

    • Shoulder angle fluctuates (60-120°) in early steps
    • Elbow maintains high angles (140-170°) throughout
    • Release phase shows optimal alignment (160-175°)

2. Critical Phase Analysis

2.1 Penultimate Step (Step 6)

  • Distinctive Features
    • Dominant side of hip reaches optimal angle (70-80°)
    • Non-dominant knee achieves peak extension (165-175°)
    • Trunk angles show maximal coordination
    • Elbow position begins final preparation (140-150°)

2.2 Release Phase Characteristics

  • Upper Body Alignment

    • Shoulder-elbow-trunk alignment at 160-180°
    • Consistent across successful throws
    • Indicates optimal power position
  • Lower Body Support

    • Dominant side of hip stabilized at 70-80°
    • Non-dominant knee maintained at 160-170°
    • Creates stable base for force generation
  • Final Position Optimization

    • Full extension of throwing arm (165-175°)
    • Shoulder-trunk alignment (160-170°)
    • Stable hip positioning (75-85°)
    • Coordinated knee extension (160-170°)
  • Joint Sequencing

    • Coordinated progression from lower to upper body
    • Full extension of throwing arm
    • Balanced trunk position
    • Maintained knee extension

3. Performance Correlations

3.1 Distance Factors

Throws achieving greater distances (85-90m) consistently display:

  • More stable progression of hip angles
  • Better maintained knee extension
  • Superior trunk coordination
  • Optimal shoulder-elbow positioning at release

3.2 Technical Consistency

  • Minimal angle variability in final phases
  • Synchronized joint movements
  • Efficient energy transfer through kinetic chain
  • Balanced body positioning throughout

4. Technical Implications

4.1 Key Success Factors

  1. Approach Phase

    • Progressive angular changes
    • Maintained stability in key joints
    • Coordinated movement patterns
  2. Release Phase

    • Consistent final position
    • Full arm extension
    • Balanced body position
    • Strong core engagement
    • Lower body support

4.2 Critical Technical Elements

  • Maintain high elbow through release
  • Achieve full shoulder extension
  • Control trunk rotation
  • Sustain knee extension
  • Coordinate hip positioning
  • Efficient energy transfer patterns

Conclusion

The implementation of pose estimation machine learning models has successfully demonstrated its capability to simulate javelin flight distances and analyze throwing techniques. This research provides dual benefits: technical advancement in sports analysis and detailed biomechanical insights for performance enhancement.

Technical Achievement

  • Successfully deployed machine learning models for pose estimation in javelin throwing
  • Achieved encouraging accuracy despite limited training data
  • Established a reliable framework for automated biomechanical analysis

Biomechanical Insights

The analysis revealed several critical patterns that differentiate successful throws:

  1. Sequential Body Mechanics

    • Progressive stabilization from approach to release
    • Optimal joint angles maintain consistency in successful throws
    • Critical angular ranges identified for key body segments:
      • Hip angles: 90-120°
      • Knee extension: 140-180°
      • Trunk coordination: 140-160°
      • Release position: 160-180° shoulder-elbow-trunk alignment
  2. Performance Correlations

    • Longer throws (85-90m) consistently demonstrate:
      • Superior joint angle coordination
      • Reduced variability in final positions
      • Efficient energy transfer through the kinetic chain
      • Stable trunk and lower body positions

Practical Applications

This research provides concrete guidance for athletes and coaches:

  1. Athletes can use the identified angular ranges as benchmarks for technique refinement
  2. Coaches can implement more precise technical feedback using the step-by-step analysis
  3. Training programs can be optimized based on quantified biomechanical parameters

Future Implications

The combination of machine learning and biomechanical analysis opens new avenues for:

  1. Real-time technique analysis during training
  2. Personalized technique optimization
  3. Injury prevention through improved form
  4. More efficient training methodologies

The results obtained demonstrate both the technical validity of the machine learning approach and its practical utility in performance enhancement. The identified biomechanical patterns provide a scientific foundation for technique development, while the pose estimation system offers a practical tool for implementation. This dual achievement suggests significant potential for advancing both the understanding and practice of javelin throwing technique.

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