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Sensors 2019, 19(3), 646; https://doi.org/10.3390/s19030646

Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring

1
Department of Computer Science, University of Rostock, 18051 Rostock, Germany
2
Department of Electrical and Electronic Engineering, University of Bristol, Bristol BS8 1UB, UK
3
Department of Computer Science, University of Bristol, Bristol BS8 1UB, UK
4
Department of Communications Engineering, University of Rostock, 18051 Rostock, Germany
5
Department of Computer Science, University of Toulon, 83957 Toulon, France
*
Author to whom correspondence should be addressed.
Current address: Department of Computer Science, University of Rostock, 18051 Rostock, Germany.
This work is an extended version of a work in progress presented in: Yordanova et al. What’s cooking and why? Behaviour recognition during unscripted cooking tasks for health monitoring. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops, Kona, HI, USA, 13–17 March 2017. The work significantly differs from the original work.
Received: 10 January 2019 / Revised: 30 January 2019 / Accepted: 1 February 2019 / Published: 4 February 2019
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Abstract

Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient’s health. To be successful, such system has to reason about the person’s actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person’s behaviour with probabilistic inference to reason about one’s actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person’s cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM. View Full-Text
Keywords: activity recognition; plan recognition; goal recognition; behaviour monitoring; symbolic models; probabilistic models; sensor-based reasoning activity recognition; plan recognition; goal recognition; behaviour monitoring; symbolic models; probabilistic models; sensor-based reasoning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yordanova, K.; Lüdtke, S.; Whitehouse, S.; Krüger, F.; Paiement, A.; Mirmehdi, M.; Craddock, I.; Kirste, T. Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring. Sensors 2019, 19, 646.

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