AI personal trainer concept using computer vision
What this concept would cover
On-device pose estimation, form scoring, personalised plan engine
The Challenge
A fitness startup wants to use the phone camera to analyse exercise form in real time, without requiring a GPU server for inference.
The Solution
On-device MediaPipe pose estimation plus a TensorFlow Lite form-scoring model designed to run at real-time frame rates on mid-range phones, with a backend that generates personalised plans from workout history and no cloud inference required.
Model Research
MediaPipe Pose benchmark vs BlazePose, TFLite quantisation strategy, latency budgeting.
Training Data
Labelled exercise reps across multiple movements with certified trainers.
On-Device Model
Int8 quantised TFLite model, Flutter plugin bridging native camera feed.
Plan Engine
Periodisation algorithm generating weekly mesocycles from fitness goals and history.
Launch
TestFlight beta followed by App Store launch.
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