Dirichlet Process Gaussian Mixture Model for Activity Discovery in Smart Homes with Ambient Sensors

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Nguyen, Thuong; Zhang, Qing; Le, Duc; Karunanithi, Mohan ORCID ID icon


2017-11-07


Conference Material


EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Melbourne, Australia, 6-9/11/2017


10


Most of the existing approaches to activity recognition in smart homes rely on supervised learning with well annotated sensor data. However obtaining such labeled data is not only challenging but sometimes also an unobtainable task, especially for senior citizens who may suffer various mental health disorders. Other unsupervised learning approaches to activity discovery are based on fixed complexity models that require the number of activities to be specified in advance. Such models may not be suitable for smart home setting as the activity space may change over time. In this paper, we propose to use a Bayesian nonparametric clustering method to discover the activities from ambient sensors deployed in smart homes. Our model can automatically infer the number of activities from observed data, thus can be widely applicable in smart home environment. We test our method on two smart home datasets, including a public dataset and a dataset collected in our project. The experiment results demonstrate the efficiency of our method in activity discovery in smart home environment.


EAI


Activity discovery; smart home; ambient sensing; unsupervised learning; Bayesian nonparametric; Dirichlet process


Ubiquitous Computing; Pattern Recognition and Data Mining


EP173997


Conference Paper - Refereed


English


Nguyen, Thuong; Zhang, Qing; Le, Duc; Karunanithi, Mohan. Dirichlet Process Gaussian Mixture Model for Activity Discovery in Smart Homes with Ambient Sensors. In: EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services; 6-9/11/2017; Melbourne, Australia. EAI; 2017. 10. http://hdl.handle.net/102.100.100/87697?index=1



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