A Hierarchical Hidden Markov Model to support activities of daily living with an assistive robotic walker

Patel, M; Miro, JV; Dissanayake, G

A Hierarchical Hidden Markov Model to support activities of daily living with an assistive robotic walker

Patel, M Miro, JV

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2012, pp. 1071 - 1076
Issue Date:: 2012-10-18

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Field	Value	Language
dc.contributor.author	Patel, M	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2012-10-18	en_US
dc.identifier.citation	Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2012, pp. 1071 - 1076	en_US
dc.identifier.isbn	9781457711992	en_US
dc.identifier.issn	2155-1774	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32307
dc.description.abstract	This paper proposes a Hierarchical Hidden Markov Model (HHMM) framework as the most suitable tool to exploit the interactions between an intelligent mobility aid and their human operator. The framework presented is capable of learning a mixed array of the Activities of Daily Living (ADL) that the typical user of these supportive devices would normally engage in, both navigational and non-navigational in nature, and provide assistance as and when required. The main contribution of this paper is the demonstration of how this probabilistic tool capable of modelling behaviours at multiple levels of abstraction is a natural embodiment of machine intelligence to support user activities. Effectiveness of the proposed HHMM framework is evaluated with a number of healthy volunteers using a conventional rolling walker equipped with sensing and navigational aids whilst operating in a structured environment resembling a home. A comparison with more traditional discriminative models and mixed generative-discriminative models is also presented to provide a complete picture that highlights the benefits of the proposed approach. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics	en_US
dc.relation.isbasedon	10.1109/BioRob.2012.6290936	en_US
dc.title	A Hierarchical Hidden Markov Model to support activities of daily living with an assistive robotic walker	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Rome, ITALY
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

Abstract:

This paper proposes a Hierarchical Hidden Markov Model (HHMM) framework as the most suitable tool to exploit the interactions between an intelligent mobility aid and their human operator. The framework presented is capable of learning a mixed array of the Activities of Daily Living (ADL) that the typical user of these supportive devices would normally engage in, both navigational and non-navigational in nature, and provide assistance as and when required. The main contribution of this paper is the demonstration of how this probabilistic tool capable of modelling behaviours at multiple levels of abstraction is a natural embodiment of machine intelligence to support user activities. Effectiveness of the proposed HHMM framework is evaluated with a number of healthy volunteers using a conventional rolling walker equipped with sensing and navigational aids whilst operating in a structured environment resembling a home. A comparison with more traditional discriminative models and mixed generative-discriminative models is also presented to provide a complete picture that highlights the benefits of the proposed approach. © 2012 IEEE.

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http://hdl.handle.net/10453/32307