This repository contains code and resources for conducting gesture recognition using EMG (Electromyography) signal data. The dataset used in this project can be found on Kaggle here.
|
The dataset consists of raw EMG data recorded from individuals wearing a MYO Thalmic bracelet on their forearm while performing various hand gestures. Each gesture is labeled with a specific category, and the dataset includes information such as time stamps and channel readings from the sensors on the bracelet.
In this project, we conducted the following steps to preprocess the data and classify the gestures:
-
Data Preprocessing:
- Cleaned the dataset to handle any missing or erroneous data points.
- Applied sliding window Fourier transform to segment the EMG signals into smaller windows, which helps capture temporal patterns in the data.
- Extracted features from the Fourier transformed signals to reduce dimensionality and focus on relevant information for classification.
-
Model Selection:
- Experimented with various deep learning models suitable for time-series data classification, including:
- AlexNet
- ResNet18
- Resnet34
- VGGNet16
- ZFNet
- RCCGNET
- Chose models with architectures capable of handling 1D input data while maintaining the convolutional and pooling layers for feature extraction.
- Experimented with various deep learning models suitable for time-series data classification, including:
-
Model Training and Evaluation:
- Split the preprocessed data into training and testing sets uisng k-fold Cross Validation.
- Trained each selected model on the training data using appropriate loss functions and optimization techniques.
- Evaluated the trained models on the test data to assess their performance in classifying different gesture categories.
- Utilized metrics such as accuracy, precision, recall and F1-score to measure the classification performance of each model.