Conference Paper/Proceeding/Abstract 307 views
Recognition of unscripted kitchen activities and eating behaviour for health monitoring / Adeline, Paiement
Proceedings of the 2nd IET International Conference on Technologies for Active and Assisted Living, Pages: 1 - 6
Swansesa University Authors: Adeline, Paiement, Adeline, Paiement
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DOI (Published version): 10.1049/ic.2016.0050
Abstract
Nutrition related health conditions such as diabetes and obesity can seriously impact quality of life for those who are affected by them. A system able to monitor kitchen activities and patients’ eating behaviours could provide clinicians with important information helping them to improve patients’...
Published in: | Proceedings of the 2nd IET International Conference on Technologies for Active and Assisted Living |
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ISBN: | 978-1-78561-393-7 |
Published: |
London
2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)
2016
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Online Access: |
http://ieeexplore.ieee.org/document/7801334/ |
URI: | https://cronfa.swan.ac.uk/Record/cronfa31411 |
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2017-08-07T10:38:51.3727064 v2 31411 2016-12-09 Recognition of unscripted kitchen activities and eating behaviour for health monitoring f50adf4186d930e3a2a0f9a6d643cf53 0000-0001-5114-1514 Adeline Paiement Adeline Paiement true false f50adf4186d930e3a2a0f9a6d643cf53 Adeline Paiement Adeline Paiement true false 2016-12-09 SCS Nutrition related health conditions such as diabetes and obesity can seriously impact quality of life for those who are affected by them. A system able to monitor kitchen activities and patients’ eating behaviours could provide clinicians with important information helping them to improve patients’ treatments. We propose a symbolic model able to describe unscripted kitchen activities and eating habits of people in home settings. This model consists of an ontology which describes the problem domain, and a Computational State Space Model (CSSM) which is able to reason in a probabilistic manner about a subject’s actions, goals, and causes of any problems during task execution. To validate our model we recorded 15 unscripted kitchen activities involving 9 subjects, with the video data being annotated according to the proposed ontology schemata. We then evaluated the model’s ability to recognise activities and potential goals from action sequences by simulating noisy observations from the annotations. The results showed that our model is able to recognise kitchen activities with an average accuracy of 80% when using specialised models, and with an average accuracy of 40% when using the general model. Conference Paper/Proceeding/Abstract Proceedings of the 2nd IET International Conference on Technologies for Active and Assisted Living 1 6 2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016) London 978-1-78561-393-7 24 10 2016 2016-10-24 10.1049/ic.2016.0050 http://ieeexplore.ieee.org/document/7801334/ COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2017-08-07T10:38:51.3727064 2016-12-09T12:12:34.1618048 College of Science Computer Science S. Whitehouse 1 K. Yordanova 2 A. Paiement 3 M. Mirmehdi 4 Adeline Paiement 5 |
title |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
spellingShingle |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring Adeline, Paiement Adeline, Paiement |
title_short |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
title_full |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
title_fullStr |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
title_full_unstemmed |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
title_sort |
Recognition of unscripted kitchen activities and eating behaviour for health monitoring |
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f50adf4186d930e3a2a0f9a6d643cf53 f50adf4186d930e3a2a0f9a6d643cf53 |
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f50adf4186d930e3a2a0f9a6d643cf53_***_Adeline, Paiement f50adf4186d930e3a2a0f9a6d643cf53_***_Adeline, Paiement |
author |
Adeline, Paiement Adeline, Paiement |
format |
Conference Paper/Proceeding/Abstract |
container_title |
Proceedings of the 2nd IET International Conference on Technologies for Active and Assisted Living |
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publishDate |
2016 |
institution |
Swansea University |
isbn |
978-1-78561-393-7 |
doi_str_mv |
10.1049/ic.2016.0050 |
publisher |
2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016) |
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College of Science |
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College of Science |
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Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science |
url |
http://ieeexplore.ieee.org/document/7801334/ |
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description |
Nutrition related health conditions such as diabetes and obesity can seriously impact quality of life for those who are affected by them. A system able to monitor kitchen activities and patients’ eating behaviours could provide clinicians with important information helping them to improve patients’ treatments. We propose a symbolic model able to describe unscripted kitchen activities and eating habits of people in home settings. This model consists of an ontology which describes the problem domain, and a Computational State Space Model (CSSM) which is able to reason in a probabilistic manner about a subject’s actions, goals, and causes of any problems during task execution. To validate our model we recorded 15 unscripted kitchen activities involving 9 subjects, with the video data being annotated according to the proposed ontology schemata. We then evaluated the model’s ability to recognise activities and potential goals from action sequences by simulating noisy observations from the annotations. The results showed that our model is able to recognise kitchen activities with an average accuracy of 80% when using specialised models, and with an average accuracy of 40% when using the general model. |
published_date |
2016-10-24T03:49:18Z |
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1652231303077036032 |
score |
10.87241 |