Show simple item record

dc.contributor.advisorQahwaji, Rami S.R.
dc.contributor.advisorKamala, Mumtaz A.
dc.contributor.authorSheraz, Nasir
dc.date.accessioned2022-07-27T14:27:09Z
dc.date.available2022-07-27T14:27:09Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10454/19077
dc.description.abstractHuman activity recognition based on wearable sensors’ data is quite an attractive subject due to its wide application in the fields of healthcare, wellbeing and smart environments. This research is also focussed on predictive performance comparison of machine learning algorithms for activity recognition from wearable sensors’ (MHEALTH) data while employing a comprehensive process. The framework is adapted from well-laid data science practices which addressed the data analyses requirements quite successfully. Moreover, an Analysis Tool is also developed to support this work and to make it repeatable for further work. A detailed comparative analysis is presented for five multi-class classifier algorithms on MHEALTH dataset namely, Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forests (RF). Beside using original MHEALTH data as input, reduced dimensionality subsets and reduced features subsets were also analysed. The comparison is made on overall accuracies, class-wise sensitivity and specificity of each algorithm, class-wise detection rate and detection prevalence in comparison to prevalence of each class, positive and negative predictive values etc. The resultant statistics have also been compared through visualizations for ease of understanding and inference. All five ML algorithms were applied for classification using the three sets of input data. Out of all five, three performed exceptionally well (SVM, KNN, RF) where RF was best with an overall accuracy of 99.9%. Although CART did not perform well as a classification algorithm, however, using it for ranking inputs was a better way of feature selection. The significant sensors using CART ranking were found to be accelerometers and gyroscopes; also confirmed through application of predictive ML algorithms. In dimensionality reduction, the subset data based on CART-selected features yielded better classification than the subset obtained from PCA technique.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectMachine learningen_US
dc.subjectR-Baseden_US
dc.subjectMHEALTH dataseten_US
dc.subjectData analysisen_US
dc.subjectActivity recognitionen_US
dc.subjectAlgorithmsen_US
dc.subjectWearable sensorsen_US
dc.subjectWearable devicesen_US
dc.titleComparative Analysis of Machine Learning Algorithms on Activity Recognition from Wearable Sensors’ MHEALTH dataset Supported with a Comprehensive Process and Development of an Analysis Toolen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnameMPhilen_US
dc.date.awarded2019
refterms.dateFOA2022-07-27T14:27:09Z


Item file(s)

Thumbnail
Name:
17019846 - Nasir Sheraz_17019846 ...
Size:
3.521Mb
Format:
PDF
Description:
MPhil Thesis

This item appears in the following Collection(s)

Show simple item record