Distributed code assignments for CDMA Packet Radio Network
IEEE/ACM Transactions on Networking (TON)
Wireless integrated network sensors
Communications of the ACM
Habitat monitoring: application driver for wireless communications technology
SIGCOMM LA '01 Workshop on Data communication in Latin America and the Caribbean
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Application-specific protocol architectures for wireless networks
Application-specific protocol architectures for wireless networks
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
CASSANDRA: audio-video sensor fusion for aggression detection
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Toward scalable activity recognition for sensor networks
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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Recent development of sensor technology gives us the opportunity to effectively monitor daily activities of individuals. As such, in this paper we present a distributed technique to recognize Activities of Daily Living (ADLs) using simple sensors. We consider a number of randomly deployed sensors in home environment augmented with home appliances (e.g., cabinet, desk, chair etc.). Our proposal consists of three major steps. At first, in a random arrangement of sensors, their triggering pattern under human actions is recorded. These records are assembled for meaningful information. This is followed by the categorization of the key sensors (i.e., most important sensors) for each activity from the acquired knowledge. Finally, we group the sensors such that activity based hierarchical clusters can be formed. The system is thus ready for activity recognition. Experiments reveal that even for a small dataset, our proposal can find out the key sensors and form clusters. Also, it is observed that our proposed mechanism yields an accuracy of determination is more than 61%. In addition, it ensures distribution of processing loads among the sensors themselves and thus minimizes the centralized processing overheads.