Ulster University Logo

Using model-based clustering to discretise duration information for activity recognition

McClean, S, Garg, Lalit, Chaurasia, Priyanka, Bryan, Scotney and Nugent, Chris (2011) Using model-based clustering to discretise duration information for activity recognition. In: Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 27-30 June, 2011, Bristol, United Kingdom, Bristol. IEEE Computer Society. Vol CBMS 2 7 pp. [Conference contribution]

Full text not available from this repository.

URL: http://dx.doi.org/10.1109/CBMS.2011.5999160

DOI: 10.1109/CBMS.2011.5999160

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.

Item Type:Conference contribution (Paper)
Keywords:Activity recognition, Duration
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:33127
Deposited By: Dr Priyanka Chaurasia
Deposited On:26 Jan 2016 16:14
Last Modified:26 Jan 2016 16:14

Repository Staff Only: item control page