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Machine Learning · Major Project

EMG Hand Gesture Recognition

Classifies hand gestures from forearm muscle signals

3-4 weeks build

Overview

Uses the public UCI EMG Data for Gestures set, with optional NinaPro data, to recognise hand gestures from surface EMG channels. Raw signals are filtered, windowed and turned into classic time-domain features for SVM and random forest models, which are compared with a 1D CNN trained on raw windows. Evaluation holds out whole subjects to test how well it generalises.

What makes it stand up

  • 01Band-pass and notch filtering of raw EMG signals
  • 02Time-domain features: MAV, RMS, waveform length, zero crossings
  • 03Classic classifiers compared with a 1D CNN on raw windows
  • 04Leave-one-subject-out evaluation, not just random splits
  • 05Signal playback viewer with the predicted gesture overlaid

Module breakdown

  1. 01

    Signal loading

    Parses multi-channel recordings and gesture labels.

  2. 02

    Filtering

    SciPy filters and normalisation per channel.

  3. 03

    Feature extraction

    Sliding windows with standard EMG features.

  4. 04

    Models

    SVM, random forest and a Keras 1D CNN.

  5. 05

    Viewer

    Streamlit playback of signals and predictions.

After this, you will be able to

  • Explain what each EMG feature captures about muscle activity
  • Defend leave-one-subject-out as the honest evaluation
  • Demo gesture predictions on recordings from an unseen subject

Technology stack

  • Python
  • NumPy
  • SciPy
  • scikit-learn
  • TensorFlow
  • Streamlit

Complete kit

₹7,499₹12,799
-41%
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Delivered in under 48 hours

Level
Major Project
Domain
Machine Learning
Build time
3-4 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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