Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion.

Bourahmoune, K; Ishac, K; Amagasa, T

Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion.

Bourahmoune, K Ishac, K

Amagasa, T

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (14), pp. 5337
Issue Date:: 2022-07-17

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Field	Value	Language
dc.contributor.author	Bourahmoune, K
dc.contributor.author	Ishac, K https://orcid.org/0000-0001-6215-3297
dc.contributor.author	Amagasa, T
dc.date.accessioned	2023-03-15T05:52:10Z
dc.date.available	2022-07-11
dc.date.available	2023-03-15T05:52:10Z
dc.date.issued	2022-07-17
dc.identifier.citation	Sensors (Basel), 2022, 22, (14), pp. 5337
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/167361
dc.description.abstract	We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system's performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method's performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22145337
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Posture
dc.subject.mesh	Recognition, Psychology
dc.subject.mesh	Sensation
dc.subject.mesh	Sitting Position
dc.subject.mesh	Humans
dc.subject.mesh	Sensation
dc.subject.mesh	Posture
dc.subject.mesh	Machine Learning
dc.subject.mesh	Sitting Position
dc.subject.mesh	Recognition, Psychology
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Posture
dc.subject.mesh	Recognition, Psychology
dc.subject.mesh	Sensation
dc.subject.mesh	Sitting Position
dc.title	Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-15T05:52:01Z
pubs.issue	14
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	14

Abstract:

We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system's performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method's performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.

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http://hdl.handle.net/10453/167361