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
Modeling the progression of Alzheimer's disease for cognitive assistance in smart homes
User Modeling and User-Adapted Interaction
Multi-Camera Human Activity Monitoring
Journal of Intelligent and Robotic 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
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The generation of explanations within evidential reasoning systems
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Object relevance weight pattern mining for activity recognition and segmentation
Pervasive and Mobile Computing
Implementing evidential activity recognition in sensorised homes
Technology and Health Care
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
Internet of things: a review of literature and products
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
Data based segmentation and summarization for sensor data in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Efficient and accurate sensor network localization
Personal and Ubiquitous Computing
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Within a smart environment, sensors have the ability to perceive changes of the environment itself and can therefore be used to infer high-level information such as activity behaviours. Sensor events collected over a period of time may contain several activities. The fundamental process of any automatic activity monitoring system is therefore to process streams of sensor events and detect occurrences of activities. In this study, we propose three segmentation algorithms to separate time series sensor data into segments to be further processed by an activity recognition algorithm. A preliminary evaluation of the approaches developed has been conducted on a data set collected from a single person living in an apartment over a period of 28 days. The results show that the proposed approaches can segment sensor data to detect activities and infer sensor segments to recognise activities with accuracies of 81.6, 81.6 and 82.9%, respectively.