CodigoTesis

Hand Gesture Recognition Applied to the Interaction with Video Games

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Introduction

This document lists the steps needed to create the CNN-LSTM model and Evaluate the result in: .

Description

Hand Gesture Recognition system development used CRISP-ML as a process model for the machine learning application lifecycle. The table shown below contains all the project files related to activities performed in the lifecycle of the HGR system.

Activity	Branch	File
Data preparation (sEMG spectrogram generation)	StaticModel	spectogramDatasetGeneration.py
Data preparation (quaternion spectrogram generation)	DynamicModel	spectogramDatasetGenerationQuat.py
Individual modeling and evaluation (static gesture model)	StaticModel	staticModel.py
Individual modeling and evaluation (dynamic gesture model)	DynamicModel	dynamicModel.py
Individual modeling and evaluation (switch classifier)	SwitchModel	switchModel.py
Evaluation App (generation of JSON )	EvaluationApp	evaluationApp.py