Vision Transformers

Vision Transformers (ViTs) are a type of deep learning model designed for computer vision tasks. Introduced by researchers at Google in 2020, they adapt the transformer architecture, originally developed for natural language processing, to handle image data.

Each row represents a different phase of the golf swing in the video sequence for analysis.

Frame	Keypoint Annotations	Club Path	Angles (Joints)	Shot Outcome
Frame 1	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Start position	Shoulder-hip (40°), Wrist angle (15°)	Setup position
Frame 10	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Mid-backswing	Shoulder-hip (70°), Wrist angle (30°)	Backswing phase
Frame 20	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Top-backswing	Shoulder-hip (90°), Wrist angle (45°)	Max backswing
Frame 30	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Downswing start	Shoulder-hip (80°), Wrist angle (30°)	Transition phase
Frame 40	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Impact	Shoulder-hip (50°), Wrist angle (15°)	Impact
Frame 50	Head (x1, y1), Shoulders (x2, y2), Hips (x3, y3)	Follow-through	Shoulder-hip (30°), Wrist angle (5°)	Post-impact

Full Sequence with Body Postures and Club Movement:

Frame	Phase	Head	Shoulder (L/R)	Elbow (L/R)	Wrist (L/R)	Hip (L/R)	Knee (L/R)	Clubhead (C)	CSA (°)	Swing Metrics
Frame 1	Setup	(25, 35)	(50, 70), (75, 70)	(55, 95), (70, 95)	(50, 120), (75, 120)	(60, 200), (85, 200)	(65, 250), (85, 250)	(100, 250)	60°	Start of the swing
Frame 10	Early Backswing	(25, 38)	(50, 75), (75, 75)	(60, 100), (70, 100)	(55, 115), (80, 115)	(60, 205), (85, 205)	(65, 252), (85, 252)	(110, 245)	70°	Club moving upward
Frame 20	Mid Backswing	(25, 42)	(50, 80), (75, 80)	(65, 105), (75, 105)	(60, 110), (85, 110)	(60, 210), (85, 210)	(65, 254), (85, 254)	(120, 230)	90°	Maximum shoulder rotation
Frame 30	Top of Backswing	(25, 44)	(48, 85), (78, 85)	(68, 110), (80, 110)	(65, 105), (90, 105)	(60, 212), (85, 212)	(65, 256), (85, 256)	(130, 225)	120°	Club at max height
Frame 40	Transition	(25, 43)	(48, 83), (78, 83)	(66, 108), (80, 108)	(64, 103), (90, 103)	(62, 212), (84, 212)	(65, 258), (84, 258)	(140, 215)	110°	Start of downswing
Frame 50	Downswing	(25, 42)	(48, 80), (78, 80)	(65, 105), (80, 105)	(63, 100), (89, 100)	(64, 210), (84, 210)	(65, 255), (84, 255)	(150, 200)	80°	Club accelerating downward
Frame 60	Impact	(25, 40)	(50, 75), (75, 75)	(63, 100), (78, 100)	(60, 95), (85, 95)	(65, 205), (85, 205)	(65, 250), (85, 250)	(155, 190)	0°	Ball contact
Frame 70	Early Follow-through	(25, 38)	(52, 70), (78, 70)	(65, 95), (80, 95)	(62, 90), (85, 90)	(65, 200), (85, 200)	(65, 248), (85, 248)	(160, 180)	40°	Club rising post-impact
Frame 80	Follow-through	(25, 36)	(55, 65), (80, 65)	(68, 90), (83, 90)	(65, 85), (88, 85)	(65, 195), (85, 195)	(65, 246), (85, 246)	(170, 160)	80°	Full shoulder rotation

Breakdown of Vision Transformer Input:

Patches as Keypoints:
Each frame is broken down into key body and club position coordinates, and these keypoints will form image patches.
A transformer model will learn the relationships between body movements (head, shoulder, elbow, wrist, hips) and club movement to detect patterns and deviations from optimal form.
Angles and Positions:
The angular data such as shoulder-hip rotation, club shaft angle, and knee flex will be crucial features for training the model.
These numerical inputs will help the model understand the mechanics of the swing and how they change over time.
Club Path and Joint Angles:
The club path like how the club moves throughout the swing is represented as changing positions of the clubhead in each frame.
Joint angles like shoulder rotation and wrist flexion are essential to ensure the player’s swing is efficient.
Time Steps and Sequence Data:
Each frame corresponds to a step in the sequence, which allows the transformer to analyze the swing as a continuous movement rather than isolated instances.
The relationships between consecutive frames will help the model understand transitions between swing phases.

Possible Outcomes from Vision Transformers:

Swing Quality: The transformer can provide feedback on whether the swing follows an optimal path or if there is a deviation like over-the-top downswing or incorrect wrist angles

Posture Analysis: The transformer will analyze the golfer's posture at each phase, providing feedback on incorrect spine angles, weight shifts, or knee flex that may affect shot accuracy.

Club Path Optimization: The model can assess whether the club's path is inside-out, outside-in, or straight, and suggest corrections to achieve more consistent shots.

Shot Prediction: Based on the swing, the model can predict the likely outcome of the shot (a slice, hook, or straight ball flight), helping golfers understand why certain issues (like a slice) occur.

This structured, frame-by-frame approach helps Vision Transformers treat each swing as a sequence of body positions and joint angles, providing precise feedback and improvement suggestions based on learned patterns. By training with such a dataset, the app can improve its ability to detect swing flaws, provide personalized advice, and optimize performance.