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Capturing, Modelling, Analyzing and providing Feedback in Martial Arts with Artificial Intelligence to support Psychomotor Learning Activities

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dc.contributor.authorCasas Ortiz, Alberto
dc.contributor.directorSantos, Olga C.
dc.date.accessioned2024-05-20T12:24:17Z
dc.date.available2024-05-20T12:24:17Z
dc.date.issued2020-06-01
dc.description.abstractThis Master’s Thesis explores how Artificial Intelligence (IA) can assist in the learning of psychomotor activities, specifically the learning of a martial art, through the development of an AI-based application that executes in an Android device. Martial arts are an interesting domain for this because it encompasses most of the characteristics that can be found in other psychomotor activities. Different methods for capturing, modelling and analyzing human motion, as well as providing feedback to the user have been reviewed. In addition, another bibliographical review of 27 publications has been carried out to evaluate till what extend these methods have been already applied to martial arts. For this research work, inertial methods have been selected for capturing motion. In particular, the inertial sensors of an Android device have been used for capturing the execution of a set of movements of American Kenpo Karate from 20 volunteers. The captured data was then modeled, by segmenting and labelling the movements, and smoothing the time series using Exponentially Weighted Moving Averages. The resulting dataset, formed by 240 movements, was then used for training and comparing three neural network-based classifiers: FC-ANN, 1D-CNN and LSTM. Neural networks were selected because of their ability of learn complex functions and the fact that some neural network architectures have been created specifically for analyze time series. Further, the weights learned by a neural network can be transferred to other domains through the technique known as transfer learning. Obtained results suggest that LSTM is the type of neural network that can better classify the movements studied, obtaining an accuracy of 1.0 in the training set, and an accuracy of 0.94 in the testing set. For demonstrating that those methods can be applied, an AI-based real-time Android application has been developed. This application employs the studied methods, as well as a feedback strategy created using the results of a questionnaire carried out with the purpose of identifying the issues that online learning of a psychomotor activity can entails. The application has then been tested, generating a good impression in the users. Following the open science philosophy, all contributions are shared in the GitHub repository.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14156
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsmartial arts
dc.subject.keywordshuman motion capture
dc.subject.keywordshuman motion modeling
dc.subject.keywordshuman motion analysis
dc.subject.keywordssequence analysis
dc.subject.keywordsfeedback strategies
dc.subject.keywordsamerican kenpo karate
dc.titleCapturing, Modelling, Analyzing and providing Feedback in Martial Arts with Artificial Intelligence to support Psychomotor Learning Activitieses
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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