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A system to recognize hand gestures by applying feature extraction, feature selection (PCA) and classification (SVM, decision tree, Neural Network) on the raw data captured by the sensors while performing the gestures.

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iamjagdeesh/Gesture-Recognition-System-for-American-Sign-Language

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Gesture Recognition System for American Sign Language

CSE 572 Data Mining Group 7

Assignment 1: Data collection

This is a manual task which involved performing the different gestures wearing the wrist sensors which record the data in terms of time series.

Assignment 2: Feature Extraction and Feature Selection

This phase involved augmenting raw sensor data, extracting and selecting features from the data to identify different gestures.

Assignment 3: User dependent analysis

This phase involved classifying different actions performed by each user into corresponding gestures using some of the machine learning techniques like decision tree, support vector machine and neural network.

Assignment 4: User independent analysis

This phase involved classifying different actions performed by all users into corresponding gestures using some of the machine learning techniques like decision tree, support vector machine and neural network.

Technology used: MATLAB

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A system to recognize hand gestures by applying feature extraction, feature selection (PCA) and classification (SVM, decision tree, Neural Network) on the raw data captured by the sensors while performing the gestures.

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