Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
RFID-based techniques for human-activity detection
Communications of the ACM - Special issue: RFID
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
IEEE Computer Graphics and Applications
Long-Term Activity Monitoring with a Wearable Sensor Node
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
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According to statistics and future prospects in the next few years world-wide energy consumption will increase significantly. Therefore not only more energy efficient technologies but also more extensive energy saving concepts have to be realized. We have developed an ”implicit interaction” based power saving concept, which automatically schedules and controls energy consumers depending on the recognized activities of users. Moreover, dynamically schedulable power consumption loads are shifted in time, so as to make use of the cheapest energy prices without compromising user comfort. In order to calculate the potential savings of this concept we have implemented a simulation framework executing energy consumption models on a city scale, which allows for more complex scenarios than being restricted to some devices or buildings only. Both the framework architecture as well as large scale energy consumption simulations are presented. Simulation experiments give strong evidence for our implicit energy management concept as a promising source of energy savings.