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Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible

Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible

Sook-Ling Chua, Stephen Marsland, Hans W. Guesgen
Copyright: © 2013 |Pages: 25
ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835
DOI: 10.4018/978-1-4666-3682-8.ch005
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MLA

Chua, Sook-Ling, et al. "Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible." Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, IGI Global, 2013, pp. 86-110. https://doi.org/10.4018/978-1-4666-3682-8.ch005

APA

Chua, S., Marsland, S., & Guesgen, H. W. (2013). Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible. In H. Guesgen & S. Marsland (Eds.), Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security (pp. 86-110). IGI Global. https://doi.org/10.4018/978-1-4666-3682-8.ch005

Chicago

Chua, Sook-Ling, Stephen Marsland, and Hans W. Guesgen. "Towards Behaviour Recognition with Unlabelled Sensor Data: As Much as Necessary, as Little as Possible." In Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, 86-110. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8.ch005

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Abstract

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

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