🌟 MotionLab — Project Reflection

Democratizing running biomechanics through AI-powered video analysis

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✨ Inspiration

MotionLab was inspired by a simple question:

"Why can't runners get professional biomechanical analysis instantly from just a video?"

Traditional gait analysis requires:

Barrier	Impact
💰 Expensive lab equipment	$10,000+ cost prohibits casual access
📡 Motion-capture sensors	Requires specialized hardware setup
👨‍⚕️ Expert evaluation	Dependent on trained biomechanics specialists
⏱️ Time-consuming review	Manual analysis creates long wait times

I wanted to make this accessible to everyday runners, turning a simple video into detailed insights.

The idea was to combine:

• 🤖 AI pose estimation
• 🎮 3D visualization
• 🏃 Running biomechanics
• ✨ Clean, modern UI

So MotionLab was born — an AI-powered tool that allows anyone to upload a short video and instantly understand their running mechanics.

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📚 What I Learned

Building MotionLab taught me more than just technical skills:

🤖 AI / ML

• How pose estimation models (MediaPipe, MoveNet, or similar architectures) extract keypoints from human motion
• Converting 2D poses into consistent, smooth biomechanical metrics
• Understanding gait cycles: stance, swing, heel strike, toe-off, cadence, symmetry, hip drop, etc.

🎨 Frontend & UI/UX

• Designing a clean, futuristic dashboard to visualize biomechanics
• Presenting complex data in a simple, intuitive way
• Creating 3D skeleton animations for better interpretation

🎬 Video Processing

• Handling different video lengths, resolutions, and aspect ratios
• Automatically trimming overly long or oversized videos
• Ensuring stable detection even in imperfect conditions

💡 Product Thinking

• What runners actually care about: performance, injury prevention, form correction
• How to simplify an AI-heavy workflow into a seamless user experience
• Clear messaging, branding, and a consistent visual style

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🛠 How I Built It

MotionLab was built through a combination of four key systems:

1️⃣ Pose Estimation Pipeline

Video Upload → Frame Extraction → AI Keypoint Detection → Data Normalization

• Extract frames from uploaded MP4 videos
• Apply AI-based pose estimation to detect keypoints
• Smooth and normalize the data for accurate biomechanical analysis

2️⃣ Biomechanics Engine

Raw Keypoints → Angle Calculation → Metric Scoring → Issue Detection

• Measure forward lean, cadence, symmetry, hip drop, knee angles, etc.
• Identify common running form issues like overstriding or valgus collapse
• Score each metric and generate personalized insights

3️⃣ Visualization Layer

Pose Data → 3D Skeleton → Metric Cards → Gait Cycle Charts

• Generate the 3D skeleton animation for playback
• Display metric cards, color-coded results, and gait cycle breakdowns
• Present injury risk and recommendations clearly

4️⃣ MotionLab UI

Upload → Processing → Results

• Dark, modern, neon-accented interface
• Simple upload flow with clear visual feedback
• Auto-cutting and preprocessing to handle user-uploaded content smoothly

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⚠️ Challenges I Faced

1. Accuracy with Real-World Videos

Not all videos are clean:

Challenge	Solution
📐 Different angles	Multi-perspective preprocessing
💡 Poor lighting	Contrast enhancement & thresholding
📹 Shaky cameras	Frame stabilization logic
🏃 Runners partially out of frame	Fallback & confidence-based filtering

I had to build preprocessing + fallback logic to keep the analysis stable.

2. Smooth Keypoint Tracking

Raw pose keypoints often:

• ❌ Jump between frames
• ❌ Flicker inconsistently
  
• ❌ Mis-detect limbs

Solution: Implemented smoothing algorithms to produce stable metrics and animations.

3. Translating AI Output into Human-Friendly Insights

AI Gives	Runners Want
Raw numbers	Easy-to-read metrics
Joint angles	Clear explanations
Statistical data	Practical coaching tips

I had to convert biomechanical data into actionable advice.

4. Building an Intuitive, Premium UI

Designing and integrating:

• 🦴 3D skeleton viewer
• 📊 Metric dashboards
• 📈 Gait cycle charts
• ✨ Animated transitions

...while keeping everything fast and responsive was a huge challenge.

5. Making the Processing Feel "Instant"

The goal was to keep the experience magic:

Upload → Wait a few seconds → Detailed analysis appears ✨

Achieving this required:

• ⚡ Code optimization
• ✂️ Efficient video trimming
• 🔄 Parallelized processing

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🚀 Closing Thoughts

MotionLab became more than an experiment — it's a step toward democratizing running biomechanics.

I turned:

Input	Output
💡 A simple idea	→
🤖 AI technology	→
🎨 Clean design	→
📚 Practical knowledge of running form	→

...into a tool runners can use to understand themselves better and improve safely.

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🏃 Built with passion for runners everywhere 🏃

MotionLab — See your run. Improve your form.

Built With

• claude
• cursor
• nextjs
• typescript
• vercel