Human 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19077 |
| Date | January 2019 |
| Creators | Sheraz, Nasir |
| Contributors | Qahwaji, Rami S.R., Kamala, Mumtaz A. |
| Publisher | University of Bradford, Faculty of Engineering and Informatics |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, MPhil |
| 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>. |
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