Computer vision coaching for football players who train without coaches, cameras, or data.
by Muzaffar Khaydarov · Mountstorm Labs
Kansei (感性) is a Japanese concept meaning perceptual intelligence — the ability to sense and refine movement through feel and feedback. That's exactly what this app gives players who've never had access to it.
Point your phone camera at training. Kansei analyzes:
- Kick biomechanics — joint angles, follow-through arc, body lean at contact
- Penalty accuracy — ball trajectory mapped against target zones
- Positional load — sprint intensity, distance covered, work rate by role
- Role-specific metrics — GK distribution, CB tackle positioning, MF press coverage, FWD finishing patterns
No expensive hardware. No camera operators. No enterprise contracts. Just your phone.
Tools like Catapult, STATSports, and Hudl exist. They cost $3,000–$15,000 per team per season.
There are 50M+ youth football players in Central Asia, Southeast Asia, and Sub-Saharan Africa who train every day without a single piece of performance data. No coach tells them their plant foot is wrong. No replay shows them why the penalty went left. They improve slowly, by feel, or they don't improve at all.
Kansei is built for them.
Phone camera → pose estimation → biomechanics analysis → role-specific feedback
↓
Optional wearable (BLE) → sprint/load metrics → combined dashboard
Core pipeline:
- Video captured on standard phone (iOS / Android)
- On-device pose estimation via MediaPipe — no cloud dependency
- Joint angle extraction and movement pattern classification
- Role-specific scoring model (GK / CB / MF / FWD)
- Feedback delivered in-app — text plus visual overlay
Why on-device? A player in Fergana, Uzbekistan or Dhaka, Bangladesh shouldn't need reliable internet to get feedback on their penalty kick. Everything runs locally.
| Layer | Technology |
|---|---|
| Mobile | React Native |
| Pose estimation | MediaPipe Pose (on-device) |
| CV pipeline | Python, OpenCV |
| ML models | PyTorch |
| Optional wearable | BLE sensor integration |
| Backend (planned) | FastAPI, PostgreSQL |
Pre-prototype — active build starting now.
- Concept validated through lived experience (grew up playing football in Uzbekistan)
- Technical foundation: distributed AI inference research with Prof. Feng Wang, University of Mississippi
- Proof of fast shipping: ValueStop — AI optimizer shipped in 24hrs at Emory Hacks 2025
- Parallel work: NeuraBash — on-device LLM inference, 30% RAM reduction
- MediaPipe pose estimation prototype
- Kick biomechanics classifier v0
- iOS/Android MVP
- First 10 users — youth clubs in Uzbekistan
Phase 1 — Core CV (now)
- Pose estimation pipeline on recorded video
- Kick angle and follow-through analysis
- Basic accuracy scoring for penalty kicks
Phase 2 — Mobile MVP
- React Native app, iOS first
- Real-time feedback overlay
- Player profile and session history
Phase 3 — Wearable integration
- BLE wearable for sprint and load metrics
- Role-specific dashboards
- Team and coach mode
Phase 4 — Market expansion
- Uzbekistan pilot with youth clubs
- Localization: Uzbek and Russian
- B2B: regional football academies
I grew up in Uzbekistan. Football is everything there — every neighborhood, every school, every empty lot has a game running. But coaching infrastructure is nearly zero outside the top professional clubs. Kids train for years on pure instinct with no feedback loop.
I moved to the US at 18 and started studying CS. I'm now researching how to run AI inference on low-cost edge devices. At some point the two things connected: the technical problem I'm working on in the lab is exactly what would let me build the product my teammates back home needed.
That's Kansei.
Muzaffar (Victor) Khaydarov — @mountstorm
CS junior, University of Mississippi (Sally McDonnell Barksdale Honors College, 4.0 GPA)
Undergraduate researcher — distributed deep learning inference, Prof. Feng Wang
CS Teaching Assistant — 49 students
Incoming SWE Intern — C Spire (Summer 2026)
Originally from Tashkent, Uzbekistan
GitHub: github.com/mountstorm
LinkedIn: linkedin.com/in/muzaffar-
Email: mkhaydar@go.olemiss.edu
Pre-prototype under active development. Star the repo to follow progress.