Human Activity Recognition Using Multiple Learning & XAI Techniques with Wearable Sensor Data

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creativework.keywordsHuman Activity Recognition, XAI Techniques, Wearable Sensor Data
dc.contributor.advisorIntisar Tahmid Naheen
dc.contributor.authorMonirul Islam Mahmud
dc.contributor.authorMd Shihab Reza
dc.contributor.authorHafeza Akter
dc.contributor.id2011839042
dc.contributor.id1931229042
dc.contributor.id1922175042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-04-15
dc.date.accessioned2025-04-15T10:18:06Z
dc.date.available2025-04-15T10:18:06Z
dc.date.issued2023
dc.description.abstractHuman Activity Recognition (HAR) is an important area of research in artificial intelligence, machine learning, and ubiquitous computing. It involves identifying or predicting human actions based on sensor data. This paper investigates Human Activity Recognition (HAR), a crucial area in artificial intelligence and multiple learning. It focuses on identifying human actions using sensor data. We analyze various machine learning techniques, including Random Forest, Gradient Boosting, AdaBoost, K-Nearest Neighbors (KNN), and the Voting Classifier, as well as deep learning models like CNN, ANN, CNN ANN Hybrid, and LSTM with our collected 72,095 collected sensor data. We also employ XAI (SHAP) techniques to understand feature importance. Results indicate that, Random Forest leads with an 85% accuracy rate. Among deep learning models, ANN achieves the highest accuracy at 82%. LGB, CatBoost, and XGBoost perform well, each reaching an 84% accuracy rate. In Federated Learning, 72% accuracy is achieved by global model with ANN. We propose an app for activity detection and data collection. These findings emphasize the potential of machine learning in enhancing HAR systems, with implications for applications from healthcare to wearable technology.
dc.description.degreeUndergraduate
dc.identifier.cd600000581
dc.identifier.print-thesisTo be defined
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1093
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleHuman Activity Recognition Using Multiple Learning & XAI Techniques with Wearable Sensor Data
dc.typeThesis
oaire.citation.endPage47
oaire.citation.startPage1
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