The mobile phone as a platform for assisting the independent living ...

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In attenuation we take advantage of the relation that the intensity of the emitted signal isinversely proportional to the square of the distance from the source. Using this relationand given the signal strength at the source, the measured signal strength could be used toestimate the distance of the object from the source. Measuring the distance usingattenuation is usually less accurate than using time-of-flight especially in environmentswith many obstructions which cause a lot of signal reflection.4.2.3 Scene analysis – FingerprintingThe scene analysis technique depends on analyzing the “scene” to obtain features thatare easily compared and represented. A scene can be described either by images (imagematching technique) or by signal property readings at that location (Bluetooth, Wi-Fi).Signal properties that have been used with the fingerprinting technique in prior work areLink Quality, Received Signal Strength Indicator, Transmit Power Level, Response Rateand Signal-to-Noise ratio.Scene analysis can be static or differential. In static scene analysis, the observed featuresare looked up in the database that maps objects to locations. In differential sceneanalysis, the difference between successive scenes is tracked. Those differences will implythat the user has moved. If features are known to be at specific locations, the observercan compute its location relative to them. The advantage of such system is that it can actindependently from an external server by storing the database locally which means thatless power is required for data transmission and the privacy of the user could bemaintained. The disadvantages include the need for a pre-built lookup database of thefeatures that might need to be rebuilt if the features of the scene change.Fingerprinting is a type of scene analysis and consists of two stages:The first stage is the training stage. At specific points called reference points within theenvironment, a chosen signal property at each point is recorded and saved in a database.For the fingerprinting database to be accurate, a significant number of reference pointsshould be made available which increases the time needed for the training phase. In thecase of the image matching technique, a set of photographs for each reference pointshould be captured and saved in the database.The second phase which is the matching stage and the actual positioning stage wherethe object measures the chosen signal property at its current position and then, using amatching algorithm, the measured value is matched with vales stored in the database inorder to obtain an approximation on the user's current position. The matching could bedone on the client side which requires that the database of fingerprints is stored locally,27
thus increasing the storage and memory requirements on the client. The other option ismatching on the server side which increases the load on the server and possibly the loadon the network. The algorithm by which the location of a user could be determinedcould either be deterministic or probabilistic. When we use images as fingerprints, imageretrieval algorithms are used in order to find the best match from the constructeddatabase. A more detailed description of these techniques follows in the next chapter.28
Page 1: ATHENS UNIVERSITY OF ECONOMICS AND
Page 5 and 6: AbstractMobile phones or Personal D
Page 7 and 8: Chapter 7: Implementation of an app
Page 9 and 10: List of TablesTable 1: Advantages a
Page 11 and 12: suffering from memory and orientati
Page 13 and 14: Chapter 2Smart Homes2.1 Smart Homes
Page 15 and 16: • Automate specific tasks that a
Page 17 and 18: Fig. 1: A smart home solution 1It i
Page 19 and 20: lighting strips and passive infra-r
Page 21 and 22: The Amigo project [10] is a joint e
Page 23 and 24: However, inside a building, that un
Page 25 and 26: with co-ordinates in the form of la
Page 27 and 28: At last, the utilized technology mu
Page 29 and 30: In this work we concentrate on achi
Page 31 and 32: In order to estimate the user locat
Page 33 and 34: 4.2.1 Cell-based methodsCell-based
Page 35: correlation between distance and si
Page 39 and 40: egions. Examining images based on t
Page 41 and 42: Chapter 6Implementation of an indoo
Page 43 and 44: 6.3 Experimental environment, hardw
Page 45 and 46: Fig. 10: The database table that st
Page 47 and 48: Fig.12: The matching stageThe query
Page 49 and 50: 6.6 Future workIn this section we p
Page 51 and 52: According to the Eye Diseases Preva
Page 53 and 54: Fig. 14: Use of image processing te
Page 55 and 56: list, printing the best match as th
Page 57 and 58: Chapter 8Conclusion8.1 ConclusionMo
Page 59 and 60: [13] Gilles Virone, Norbert Noury,
Page 61 and 62: [40] Andrew M. Ladd, Kostas E. Bekr
Page 63: [65] X. Chen and A.L. Yuille, “A

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