The Z notation: a reference manual
The Z notation: a reference manual
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Service Rings - A Semantic Overlay for Service Discovery in Ad hoc Networks
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A distributed hierarchical structure for object networks supporting human activity recognition
MMNS'06 Proceedings of the 9th IFIP/IEEE international conference on Management of Multimedia and Mobile Networks and Services
Human activity recognition in pervasive health-care: Supporting efficient remote collaboration
Journal of Network and Computer Applications
Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
A Self-organizing Approach to Activity Recognition with Wireless Sensors
IWSOS '09 Proceedings of the 4th IFIP TC 6 International Workshop on Self-Organizing Systems
Recognition of user activity sequences using distributed event detection
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
| Hi-index | 0.00 |
Activity recognition has a high applicability scope in patient monitoring since it has the potential to observe patients' actions and recognise erratic behaviour. Our activity recognition architecture described in this paper is particularly suited for this task due to the fact that collaboration of constituent components, namely Object Networks, Activity Map and Activity Inference Engine create a flexible and scalable platform taking into consideration needs of individual users. We utilise information generated from sensors that observe user interaction with the objects in the environment and also information from body-worn sensors. This information is processed in a distributed manner through the object network hierarchy which we formally define. The object network has the effect of increasing the level of abstraction of information such that this high-level information is utilised by the Activity Inference Engine. This engine also takes into consideration information from the user's profiles in order to deduce the most probable activity and at the same time observe any erratic or potentially unsafe behaviour. We also present a scenario and show the results of our study.