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Wearable device localisation and its effect on activity recognition using machine learning
Dissertation (MEng (Computer Engineering))--University of Pretoria, 2019.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613688772427776 |
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| access_status_str | Open Access |
| author2 | Hancke, Gerhard P. |
| author_browse | Hancke, Gerhard P. |
| author_facet | Hancke, Gerhard P. |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Computer Engineering))--University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/70775 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:07.894Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/70775 Wearable device localisation and its effect on activity recognition using machine learning Hancke, Gerhard P. damian.dearruda@tuks.co.za Myburgh, Hermanus Carel De Arruda, Damian Phillip Caldeira Machine learning Localisation UCTD Dissertation (MEng (Computer Engineering))--University of Pretoria, 2019. Many developments have been observed from research into activity recognition. Alongside these developments, many challenges have also been identified which affect the design, implementation and evaluation of the activity recognition systems performance. One such challenge is the successful inclusion of contextual awareness in order to improve the system’s performance. This research seeks to examine the effect of localising a wearable device, in the activity recognition problem. Three machine learning models were implemented, which make use of the on-body device location in different ways. The first model contains no knowledge of the on-body device location, the second model contains the encoded location of the device as a feature in the dataset, the third model separates each dataset according to their corresponding location, with each location being treated as an independent problem. A final fourth model was proposed and implemented which attempts to closely emulate the best performing model of the previous three, while being fully autonomous. The autonomy is achieved by applying another classification step to determine the device location and then performing activity recognition. The performance of each model was tested using various combinations of feature selection algorithms and classifiers. When using no location information, model 1 generated a classification accuracy of 89%; using the location as an encoded feature inserted into the dataset, model 2 yielded a classification accuracy of 90.2%. Classification of the activities when considering training data only from the location of the wearable device, model 3 generated an average accuracy of 95.5%. The fully autonomous model 4, which was based on the activity recognition in model 3, managed to achieve a 94.5% classification accuracy. These results show that using the location of the device to give the system added context, makes a statically significant impact on the performance of the system. South African Research Chairs Initiative (SARChI) Research Chair in Advanced Sensor Networks, co-hosted by the University of Pretoria and the Council for Scientific and Industrial Research (CSIR) Meraka Institute. Centre for Connected Intelligence (CCI) at the University of Pretoria. Electrical, Electronic and Computer Engineering MEng (Computer Engineering) Unrestricted 2019-07-22T10:41:18Z 2019-07-22T10:41:18Z 2019-09-03 2019-06 Dissertation de Arruda, DPC 2019, Wearable device localisation and its effect on activity recognition using machine learning, Masters Dissertation, University of Pretoria, Pretoria S2019 http://hdl.handle.net/2263/70775 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Machine learning Localisation UCTD Wearable device localisation and its effect on activity recognition using machine learning |
| title | Wearable device localisation and its effect on activity recognition using machine learning |
| title_full | Wearable device localisation and its effect on activity recognition using machine learning |
| title_fullStr | Wearable device localisation and its effect on activity recognition using machine learning |
| title_full_unstemmed | Wearable device localisation and its effect on activity recognition using machine learning |
| title_short | Wearable device localisation and its effect on activity recognition using machine learning |
| title_sort | wearable device localisation and its effect on activity recognition using machine learning |
| topic | Machine learning Localisation UCTD |
| url | http://hdl.handle.net/2263/70775 |