Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
An unsupervised approach to activity recognition and segmentation based on object-use fingerprints
Data & Knowledge Engineering
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
A Knowledge-Driven Approach to Activity Recognition in Smart Homes
IEEE Transactions on Knowledge and Data Engineering
Workshop overview for the international workshop on situation, activity and goal awareness
Proceedings of the 13th international conference on Ubiquitous computing
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Within smart homes, ambient sensors are used to monitor the interaction between users and the home environment. The data produced from the sensors is used as the basis for the inference of the users' behaviour information. Partitioning sensor data in response to individual instances of activity is critical for a smart home to be fully functional and to fulfil its role, such as correctly measuring health status and detecting emergency situations. In this study, we propose a multi-component based sensor segmentation approach in an effort to detect and recognise activities within a smart home. A publicly available dataset containing 1,230 sensor events generated in response to 245 instances of 7 activity classes along with 59 idle periods was used to validate the approach. Initial results of the proposed methods have been encouraging achieving 95.72%, 89.52% and 86.13% in respect to precision, recall and accuracy in detecting and recognising activities in addition to the capability of detecting interleaved activities.