Mining and monitoring patterns of daily routines for assisted living in real world settings
Proceedings of the 1st ACM International Health Informatics Symposium
Activity knowledge transfer in smart environments
Pervasive and Mobile Computing
Activity recognition: an evolutionary ensembles approach
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Dynamic multi-component based activity detection and recognition within smart homes
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Complex activity recognition using context driven activity theory in home environments
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
Expert Systems with Applications: An International Journal
Embodying care in Matilda: an affective communication robot for the elderly in Australia
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Assisted living technologies for older adults
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
The autonomic computing paradigm in adaptive building / ambient intelligence systems
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Information Sciences: an International Journal
Pervasive and Mobile Computing
Online activity recognition using evolving classifiers
Expert Systems with Applications: An International Journal
Trajectory mining from anonymous binary motion sensors in Smart Environment
Knowledge-Based Systems
EEM: evolutionary ensembles model for activity recognition in Smart Homes
Applied Intelligence
Segmenting sensor data for activity monitoring in smart environments
Personal and Ubiquitous Computing
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Mining order-preserving submatrices from probabilistic matrices
ACM Transactions on Database Systems (TODS)
Derivation of night time behaviour metrics using ambient sensors
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Longitudinal residential ambient monitoring: correlating sensor data to functional health status
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
An ontology-based approach to ADL recognition in smart homes
Future Generation Computer Systems
Activity recognition on streaming sensor data
Pervasive and Mobile Computing
A method of abnormal habits recognition in intelligent space
Engineering Applications of Artificial Intelligence
Constructing the Web of Events from raw data in the Web of Things
Mobile Information Systems - Internet of Things
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
Learning a taxonomy of predefined and discovered activity patterns
Journal of Ambient Intelligence and Smart Environments
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The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.