XAI606 Course Project Description

---

I. Project Title

Optimizing Subject Independent Classification Performance in EMG-Based Gesture Recognition

---

II. Project Introduction

Objective

The primary goal is to enhance the classification accuracy of EMG signals for static hand gestures, specifically focusing on Class 1 to Class 6. We'll employ advanced neural network architectures and machine learning techniques for this. Class 0 can be used as a baseline or as deemed appropriate.

Motivation

Improving the accuracy of EMG-based gesture recognition has significant implications for BCI applications, including assistive technologies and human-computer interaction.

---

III. Dataset Description

The dataset contains raw EMG data from 36 subjects performing static hand gestures(Open data). Each subject executed two series of 6 or 7 basic gestures. Each gesture lasted for 3 seconds, with a 3-second pause between gestures. Data was collected using a MYO Thalmic bracelet equipped with eight sensors.

• Columns:

  1. Time - Time in ms 2-9) Channel - Eight EMG channels from MYO Thalmic bracelet
  2. Class - Gesture labels:
  • 0: Unmarked data
  • 1: Hand at rest
  • 2: Hand clenched in a fist
  • 3: Wrist flexion
  • 4: Wrist extension
  • 5: Radial deviations
  • 6: Ulnar deviations
  • 7: Extended palm (not performed by all subjects)

• Additional Column:

  • Label: Refers to the subject who performed the experiment

---

Dataset Download

You can download the .csv files for the dataset from this Google Drive link.

Requirement

conda 23.3.0, Python 3.11.4, torch 2.0.1+cu117

or use jyk.yaml the copy of my own env, and make conda env.

---