Client projectAILive
Wellnix
AI health platform combining motion analysis, real-time nutrition intelligence, and predictive wellness.
Result
Posture scored from phone video
The problem
Working out at home means nobody tells you your form is wrong until something hurts.
What I built
Built a computer-vision engine that scores posture and movement from ordinary phone video, and paired it with nutrition tracking so both signals feed the same picture.
The outcome
Form feedback without a trainer in the room, and recommendations that draw on movement and diet together rather than either alone.
How it works
- 01
The user records a movement on an ordinary phone camera, with no wearable and no depth sensor.
- 02
Pose estimation extracts joint positions per frame and the movement is scored against the shape the exercise should have.
- 03
Feedback comes back as the specific correction, not a score, because a number does not tell anyone what to change.
- 04
Nutrition entries feed the same profile, so recommendations reason over movement and intake together.
The AI layer
Pose estimation on video frames, then rule-based scoring of the extracted skeleton against per-exercise form criteria. The scoring is deliberately not a model: form rules are known, explainable, and easier to correct when a coach disagrees with them.
The engineering layer
Client-side capture with server-side analysis, frame sampling to keep it affordable, and a data model that keeps movement and nutrition in one profile rather than two apps.
Key technical decisions
Deterministic scoring on top of a learned model.
Pose estimation is the part only a model can do. Judging whether a knee tracked past the toe is arithmetic, and doing it in code makes every judgement explainable to the user.