A brief survey of self-organization in wireless sensor networks: Research Articles
Wireless Communications & Mobile Computing - Advances in Resource-Constrained Device Networking
Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution
Multimedia Tools and Applications
AnyBody: a self-organization protocol for body area networks
Proceedings of the ICST 2nd international conference on Body area networks
Self-organising object networks using context zones for distributed activity recognition
Proceedings of the ICST 2nd international conference on Body area networks
On-body activity recognition in a dynamic sensor network
Proceedings of the ICST 2nd international conference on Body area networks
SMASH: a distributed sensing and processing garment for the classification of upper body postures
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Where am i: recognizing on-body positions of wearable sensors
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
DarSens: a framework for distributed activity recognition from body-worn sensors
Proceedings of the Fifth International Conference on Body Area Networks
| Hi-index | 0.00 |
In this paper, we describe an approach to activity recognition, which is based on a self-organizing, ad hoc network of body-worn sensors. It makes best use of the available sensors, and autonomously adapts to dynamically varying sensor setups in terms of changing sensor availabilities, characteristics and on-body locations. For a widespread use of activity recognition systems, such an opportunistic approach is better suited than a fixed and application-specific deployment of sensor systems, as it unburdens the user from placing specific sensors at pre-defined locations on his body. The main contribution of this paper is the presentation of an interaction model for the self-organization of sensor nodes, which enables a cooperative recognition of activities according to the demands of a user's mobile device. We implemented it with an embedded system platform, and conducted an evaluation showing the feasibility and performance of our approach.