By Estefanía Serral, Pedro Valderas, Vicente Pelechano (auth.), Boris de Ruyter, Reiner Wichert, David V. Keyson, Panos Markopoulos, Norbert Streitz, Monica Divitini, Nikolaos Georgantas, Antonio Mana Gomez (eds.)
In a global supported through Ambient Intelligence (AmI), a variety of units embedded within the surroundings jointly use the allotted info and the intelligence inherent during this interconnected surroundings. quite a number details from sensing and reas- ing applied sciences is utilized by disbursed units within the setting. The cooperation among ordinary consumer interfaces and sensor interfaces covers all of a person’s s- roundings, leading to a tool setting that behaves intelligently; the time period “Ambient Intelligence” has been coined to explain it. during this manner, the surroundings is ready to realize the people in it, to spot their person wishes, to benefit from their habit, and to behave and react of their curiosity. given that this imaginative and prescient is prompted by way of loads of diversified strategies in info proce- ing and combines multi-disciplinary fields in electric engineering, desktop technology, commercial layout, person interfaces, and cognitive sciences, enormous study is required to supply new versions of technological innovation inside a multi-dimensional society. therefore the AmI imaginative and prescient depends on the large-scale integration of electronics into the surroundings, permitting the actors, i.e., humans and items, to have interaction with their surrounding in a unbroken, reliable, and typical manner.
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Extra info for Ambient Intelligence: First International Joint Conference, AmI 2010, Malaga, Spain, November 10-12, 2010. Proceedings
As the user moved closer to the supermarket, this event could evolve requiring other interaction methods more explicit such as change-based, graphical and speech. Thus, another features are activated producing a diﬀerent UI showed at the right of the ﬁgure. 3 Validation of the Proposal In order to validate our proposal, we have deﬁned a scenario that illustrates how interaction can be adapted to provide an adequate obtrusiveness level in an Ambient Intelligence (AmI) environment. The adaptation takes into account diﬀerent factors such as the location of mobile phone (at user’s hand or nearby to the user), user location and message urgency.
The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples.
36% overall accuracy as the recognition of “idle” is almost perfect (see Sec. 3: simple threshold comparison). To avoid this over-optimistic and not quite informative evaluation, we limited the set of “idle” frames to four per utensil, which were randomly selected per subject. This effectively truncates the dataset to 12,265 frames (of 64 samples each). , we trained the recognizer using the data from 20 subjects, and tested on those data recorded for the remaining subject. This process was repeated for all 21 subjects and results were averaged.
Ambient Intelligence: First International Joint Conference, AmI 2010, Malaga, Spain, November 10-12, 2010. Proceedings by Estefanía Serral, Pedro Valderas, Vicente Pelechano (auth.), Boris de Ruyter, Reiner Wichert, David V. Keyson, Panos Markopoulos, Norbert Streitz, Monica Divitini, Nikolaos Georgantas, Antonio Mana Gomez (eds.)