Human activity recognition for domestic robots

Piyathilaka, L; Kodagoda, S

Human activity recognition for domestic robots

Piyathilaka, L

Kodagoda, S

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Publication Type:: Chapter
Citation:: 2015, 105 pp. 395 - 408
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Piyathilaka, L https://orcid.org/0000-0003-4676-9387	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	2015, 105 pp. 395 - 408	en_US
dc.identifier.isbn	9783319074870	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130340
dc.description.abstract	© Springer International Publishing Switzerland 2015. Capabilities of domestic service robots could be further improved, if the robot is equipped with an ability to recognize activities performed by humans in its sensory range. For example in a simple scenario a floor cleaning robot can vacuum the kitchen floor after recognizing human activity "cooking in the kitchen". Most of the complex human activities can be sub divided into simple activities which can later used for recognize complex activities. Activities like "take meditation" can be sub divided into simple activities like "opening pill container" and "drinking water". However, even recognizing simple activities are highly challenging due to the similarities between some inter activities and dissimilarities of intra activities which are performed by different people, body poses and orientations. Even a simple human activity like "drinking water" can be performed while the subject is in different body poses like sitting, standing or walking. Therefore building machine learning techniques to recognize human activities with such complexities is non trivial. To address this issue, we propose a human activity recognition technique that uses 3D skeleton features produced by a depth camera. The algorithm incorporates importance weights for skeleton 3D joints according to the activity being performed. This allows the algorithm to ignore the confusing or irrelevant features while relying on informative features. Later these joints were ensembled together to train Dynamic Bayesian Networks (DBN), which is then used to infer human activities based on likelihoods. The proposed activity recognition technique is tested on a publicly available dataset and UTS experiments with overall accuracies of 85% and 90%.	en_US
dc.relation.isbasedon	10.1007/978-3-319-07488-7_27	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Human activity recognition for domestic robots	en_US
dc.type	Chapter
utslib.citation.volume	105	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	105	en_US

Abstract:

© Springer International Publishing Switzerland 2015. Capabilities of domestic service robots could be further improved, if the robot is equipped with an ability to recognize activities performed by humans in its sensory range. For example in a simple scenario a floor cleaning robot can vacuum the kitchen floor after recognizing human activity "cooking in the kitchen". Most of the complex human activities can be sub divided into simple activities which can later used for recognize complex activities. Activities like "take meditation" can be sub divided into simple activities like "opening pill container" and "drinking water". However, even recognizing simple activities are highly challenging due to the similarities between some inter activities and dissimilarities of intra activities which are performed by different people, body poses and orientations. Even a simple human activity like "drinking water" can be performed while the subject is in different body poses like sitting, standing or walking. Therefore building machine learning techniques to recognize human activities with such complexities is non trivial. To address this issue, we propose a human activity recognition technique that uses 3D skeleton features produced by a depth camera. The algorithm incorporates importance weights for skeleton 3D joints according to the activity being performed. This allows the algorithm to ignore the confusing or irrelevant features while relying on informative features. Later these joints were ensembled together to train Dynamic Bayesian Networks (DBN), which is then used to infer human activities based on likelihoods. The proposed activity recognition technique is tested on a publicly available dataset and UTS experiments with overall accuracies of 85% and 90%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/32238