Upload 165-Allow Motion.mp4

This revised diagram outlines a three-phase training system for our myoelectric prosthetic hand, refining the process from data collection to real-time EMG-only control.

Phase 1: Data Collection
Goal: Build a dataset pairing hand gestures with EMG signals.

Inputs:
Hand Image (Webcam): Captures visual gestures.
EMG Signal (Sensors): Records muscle activity.

Processing:
Image Processing:
Tracks hand region & detects finger positions.
Recognizes meaningful gestures (e.g., fist, pinch).

EMG Processing:
Filters and analyzes muscle signals.
Matches EMG patterns to corresponding gestures.

Output:
A training dataset linking EMG signals to gestures.

Phase 2: Matching EMG + Hand Gestures (Learning Phase)
Goal: Train AI to associate EMG signals with gestures.

Inputs: Same as Phase 1 (camera + EMG).

Processing:
EMG Signal Matching:
Compares live EMG data to the dataset.
Predicts intended gestures.

Verification:
Cross-checks predicted gestures against camera input or user input for accuracy.

Action Execution:
Prosthetic performs the recognized gesture.

Phase 3: EMG-Only Control (Use Phase)
Goal: Operate the prosthetic without camera input (EMG-driven).

Input: EMG signals only (no visual tracking).

Processing:
Signal Processing:
Analyzes muscle activity in real time.
Matches EMG patterns to pre-trained gestures.

Action Execution:
Prosthetic performs the predicted gesture (e.g., grip, release).

Key Improvements Over the Previous Version
Structured Progression:
Data Collection → AI Training → Real-World Use (EMG-only).

Reduced Dependency on Camera:
The final phase relies solely on EMG, making it more practical for daily use.

Verification Step:
Ensures AI predictions match actual gestures before full deployment.


165-Allow Motion.mp4

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