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Title: Using model-based clustering to discretise duration information for activity recognition
Authors: McClean, Sally
Garg, Lalit
Chaurasia, Priyanka
Scotney, Bryan
Nugent, Chris
Keywords: Human activity recognition
Home automation
Magnetic resonance imaging
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: McClean, S., Garg, L., Chaurasia, P., Scotney, B., & Nugent, C. (2011). Using model-based clustering to discretise duration information for activity recognition. 24th International Symposium on Computer-Based Medical Systems, Bristol. 1-7.
Abstract: Activity recognition is an important component of patient management in smart homes where high level activities can be learned from low level sensor data. Such activity recognition utilises sensor ID, task order and time of activation to learn about patient behavior, detect anomalies and provide prompts or other interventions. In this paper we use the sensor activation times to calculate durations and then investigate several model-based clustering approaches with a view to discretising the duration data and using such data to improve activity prediction. We explore several popular approaches to characterising such duration data, namely Coxian phase type distributions and Gaussian mixture distributions. We then show how we can utilise the learned clustering components for discretisation. Finally we use simulated data, based on a real smart kitchen deployment, to compare these approaches and evaluate the discretisation results with regard to activity prediction.
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