What is Biomechanical Analysis?
Biomechanical analysis is the quantitative study of human movement — measuring how joints rotate, how fast limbs move and how consistently an athlete repeats a pattern. Traditionally this required motion-capture labs with reflective markers and calibrated cameras. Today, computer vision can extract much of the same data from ordinary video, which is what makes it practical for live training and competition.
Hassan applies this at ID Sports Ventures in Berlin, where he engineers real-time video analytics that track athlete performance. The goal is never just to draw keypoints on screen — it is to convert raw pose data into numbers a coach or sports scientist can actually act on.
From Video to Joint Angles
The first step is pose estimation: a keypoint model localizes anatomical joints on every frame. Joint angles then come from simple geometry — the elbow angle is the angle formed by the shoulder, elbow and wrist keypoints, and the knee angle follows the same pattern from hip to ankle.
The details matter more than the geometry. Raw keypoints jitter frame to frame, so Hassan smooths the tracks before computing angles and clips implausible values when keypoint confidence drops. With multi-object tracking and sub-30ms keypoint models, a whole team can be measured simultaneously, with each athlete's joint data tracked under its own identity through occlusion.
Limb Velocity & Motion Quality
Angles alone describe static posture; velocity describes movement. By differentiating the smoothed keypoint positions over time, Hassan derives limb velocities, angular speeds and accelerations that capture how a movement actually unfolds — not just its final shape.
Motion quality is where biomechanical reports become useful to practitioners. Symmetry between left and right limbs, range-of-motion consistency across repetitions and deviations from a reference pattern all translate into readable scores. These feed directly into the standardized biomechanical performance reports he builds for sports scientists.
“A joint angle is a snapshot; a limb velocity is a story. Biomechanical analysis becomes useful when both are measured across time, per athlete, under their own identity.” — Muhammad Hassan Gul
Handling High-Speed Motion Blur
Fast sports movements are the worst case for keypoint models: a swinging limb can move dozens of pixels between shutter exposures, producing streaks that confuse the network and tank confidence scores. Clean training data alone is not enough for competition footage.
Hassan uses synthetic data augmentation to fix this. Clean, well-labeled poses are synthetically degraded with motion blur, noise and compression artifacts during training, so the model learns to extract keypoints from exactly the kind of messy frames it will see live. This approach contributed to a +15% pose accuracy boost on the real-time pipeline.
Working with Sports Scientists
Engineers measure pixels; sports scientists measure performance. Hassan keeps the two domains aligned by treating the biomechanical report as the product, not the model. Every metric emitted downstream is named, documented and validated against what practitioners already use — gait scores, joint-angle ranges, velocity profiles.
That means the pipeline outputs clean, standardized data (often served through a FastAPI backend) rather than raw keypoint dumps, so a scientist can consume the results without touching model internals. The engineering focus is on reliability, explainability and honest error bounds at every step.
Applications
- Performance — quantifying range of motion, symmetry and speed to track athlete development over a season.
- Injury prevention — flagging asymmetric loading or abnormal joint-angle patterns before they become chronic issues.
- Coaching — giving coaches objective feedback between sessions instead of relying on subjective observation alone.
Interested in applying these techniques to your sport or clinic? See how the full stack fits together on the sports analytics topic, or reach out via the contact section.