loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Leonardo Martins 1 ; Bruno Ribeiro 2 ; Rui Almeida 2 ; Hugo Pereira 2 ; Adelaide Jesus 2 ; Cláudia Quaresma 2 and Pedro Vieira 2

Affiliations: 1 Universidade Nova de Lisboa and UNINOVA, Portugal ; 2 Universidade Nova de Lisboa, Portugal

ISBN: 978-989-758-170-0

Keyword(s): Intelligent Chair, Pressure Sensors, Sitting Posture, Classification, Algorithmic Optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Distributed and Mobile Software Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Knowledge-Based Systems ; Mobile Technologies ; Mobile Technologies for Healthcare Applications ; Neural Rehabilitation ; Neurotechnology, Electronics and Informatics ; Pattern Recognition and Machine Learning ; Software Engineering ; Symbolic Systems

Abstract: An intelligent chair prototype was developed in order to detect and correct the adoption of bad sitting postures during long periods of time. A pneumatic system was enclosed in the chair (4 air bladders inside the seat pad and 4 in the backrest) to classify 12 standardized sitting postures, with a classification score of 80.9%. Recently we used algorithmic optimization applied to the existing classification algorithm (based on Neural Networks) to split users (using Classification Trees) by their sex and used two different previously trained Neural Networks (Male and Female) to get an improved classification of 89.0% when the user was identified and 87.1% for unidentified users. In this work we aim to investigate the usage of the anthropometric information (height and weight) to further optimize our classification process. Here we use four Machine Learning Techniques (Neural Networks, Support Vector Machines, Classification Trees and Naive Bayes) to automatically split the users in 2 c lasses (above and below the specific anthropometric median value). Results showed that Classification Trees worked best on automatically separating the body characteristics (i.e. Height) with a global optimization of 88.3%. During the classification process, if the user is identified, we skip the splitting step, and this optimization increases to 90.2%. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.191.150

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Martins, L.; Ribeiro, B.; Almeida, R.; Pereira, H.; Jesus, A.; Quaresma, C. and Vieira, P. (2016). Optimization of Sitting Posture Classification based on Anthropometric Data.In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 406-413. DOI: 10.5220/0005790104060413

@conference{healthinf16,
author={Leonardo Martins. and Bruno Ribeiro. and Rui Almeida. and Hugo Pereira. and Adelaide Jesus. and Cláudia Quaresma. and Pedro Vieira.},
title={Optimization of Sitting Posture Classification based on Anthropometric Data},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)},
year={2016},
pages={406-413},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005790104060413},
isbn={978-989-758-170-0},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2016)
TI - Optimization of Sitting Posture Classification based on Anthropometric Data
SN - 978-989-758-170-0
AU - Martins, L.
AU - Ribeiro, B.
AU - Almeida, R.
AU - Pereira, H.
AU - Jesus, A.
AU - Quaresma, C.
AU - Vieira, P.
PY - 2016
SP - 406
EP - 413
DO - 10.5220/0005790104060413

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.