Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition

While splitting datasets, researchers assume that training set is exchangeable with test set and expect good classification performance. This assumption is invalid due to subject variability due to age differences. Classification models trained on activity data from one particular age group such as...

Full description

Saved in:

Bibliographic Details
Main Author:	Jimale, Ali Olow
Format:	Thesis
Language:	English
Published:	2023
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/60094/1/ALI%20OLOW%20JIMALE%20-%20TESIS%20cut.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.60094
record_format	uketd_dc
spelling	my-usm-ep.600942024-03-11T02:19:26Z Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition 2023-07 Jimale, Ali Olow QA75.5-76.95 Electronic computers. Computer science While splitting datasets, researchers assume that training set is exchangeable with test set and expect good classification performance. This assumption is invalid due to subject variability due to age differences. Classification models trained on activity data from one particular age group such as adults cannot generalize to activity data collected from a different age group such as elderly. Subject variability in the context of age is a valid problem that degrades the performance of activity recognition, but remains unexplored. Existing studies that investigated subject variability in activity recognition overlooked this problem and only focused on contextual subject variability, and intra-subject variability. This study investigates the effects of subject variability on the performance of sensor-based activity recognition. Elderly and adult datasets were used to evaluate the assessment techniques. Experiments on adult dataset only, experiments on elderly dataset only, and experiments on adult (as training) and elderly (as test) datasets were conducted using machine learning and deep learning. The results show a significant performance drop in activity recognition on different subjects with different age groups. On average, the drop in recognition accuracy is 9.75% and 12% for machine learning and deep learning models respectively. Conditional Generative Adversarial Network (CGAN) is an ideal solution to address subject variability. 2023-07 Thesis http://eprints.usm.my/60094/ http://eprints.usm.my/60094/1/ALI%20OLOW%20JIMALE%20-%20TESIS%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Jimale, Ali Olow Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
description	While splitting datasets, researchers assume that training set is exchangeable with test set and expect good classification performance. This assumption is invalid due to subject variability due to age differences. Classification models trained on activity data from one particular age group such as adults cannot generalize to activity data collected from a different age group such as elderly. Subject variability in the context of age is a valid problem that degrades the performance of activity recognition, but remains unexplored. Existing studies that investigated subject variability in activity recognition overlooked this problem and only focused on contextual subject variability, and intra-subject variability. This study investigates the effects of subject variability on the performance of sensor-based activity recognition. Elderly and adult datasets were used to evaluate the assessment techniques. Experiments on adult dataset only, experiments on elderly dataset only, and experiments on adult (as training) and elderly (as test) datasets were conducted using machine learning and deep learning. The results show a significant performance drop in activity recognition on different subjects with different age groups. On average, the drop in recognition accuracy is 9.75% and 12% for machine learning and deep learning models respectively. Conditional Generative Adversarial Network (CGAN) is an ideal solution to address subject variability.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Jimale, Ali Olow
author_facet	Jimale, Ali Olow
author_sort	Jimale, Ali Olow
title	Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
title_short	Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
title_full	Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
title_fullStr	Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
title_full_unstemmed	Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition
title_sort	enhanced conditional generative adversarial network for handling subject variability in human activity recognition
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2023
url	http://eprints.usm.my/60094/1/ALI%20OLOW%20JIMALE%20-%20TESIS%20cut.pdf
_version_	1794024089094979584

Enhanced Conditional Generative Adversarial Network For Handling Subject Variability In Human Activity Recognition

Similar Items