Ambient Intelligence: Third International Joint Conference, by Hristijan Gjoreski, Mitja Luštrek, Matjaž Gams (auth.),

By Hristijan Gjoreski, Mitja Luštrek, Matjaž Gams (auth.), Fabio Paternò, Boris de Ruyter, Panos Markopoulos, Carmen Santoro, Evert van Loenen, Kris Luyten (eds.)

This publication constitutes the refereed court cases of the 3rd overseas Joint convention an Ambient Intelligence, AmI 2012, held in Pisa, Italy, in November 2012. The 18 revised complete papers and five brief papers awarded have been rigorously reviewed and chosen from forty seven (full papers) respectively 14 (short papers) submissions. From a systematic perspective, the papers make a multidisciplinary procedure masking fields like machine technology, human computing device interplay, electric engineering, business layout, behavioral sciences, aimed toward enriching actual environments with a community of dispensed units, similar to sensors, actuators, and computational assets, with a purpose to help clients of their daily actions. From a technological viewpoint the amount represents the convergence of modern achievements in ubiquitous and conversation applied sciences, pervasive computing, clever person interfaces and synthetic intelligence.

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In: Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC 2003), pp. 88–97. IEEE Computer Society, Washington, DC (2003) 14. : Wearable approach for continuous ECG and Activity Patient-Monitoring. In: Complexity, pp. 2184–2187 (2004) 15. : Fine-grained activity recognition by aggregating abstract object usage. In: Proceedings of the 9th IEEE International Symposium on Wearable Computers (ISWC 2005), pp. 44–51 (2005) 16. : Inferring activities from interactions with objects.

The acceleration data is often augmented with data from sensors such as gyroscopes [12], magnetometers [2], ambient light [6] or ambient and skin temperature [13], aiming to extract a more detailed environmental user context. In [26], the authors use heart rate information as an addition to the accelerometer data to detect activities like lifting and lowering loads or even digging. In [23], workshop assembly activities are detected by augmenting acceleration sensors with microphones. The works of Fishkin et al.

9] and Patterson et al. [15] show that detecting touched and used objects can be very helpful for activity recognition. By using RFID readers that are embedded in gloves or bracelets at the wrist and RFID tags attached to various objects of interest, one can detect the object grasped and used by the user, thus aiding the activity recognition in various application scenarios, such as activities of daily living [22] and [16], activity tracking in car manufacturing [21], or household and gardening activities [5].

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