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

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3.4 Demands and performance measures for indoor localizationsystemsIn order to judge an implementation, its performance parameters must be analyzed. Inthis section we discuss possible performance measures for indoor localization systems.One of the major performance parameters of a localization system is its accuracy. Thisterm indicates how far the estimated location deviates from the true location. Accuracy isoften confused with precision. Precision is a measure of the reproducibility of ameasurement. Therefore, when a statement is made about the resolution of a localizationsystem, accuracy is commonly supplemented by precision (e.g. 20 cm accuracy over 90%of the time). For the applications mentioned above, the required localization accuracyranges roughly from ±5 cm (robot positioning) to about ±1m (guidance systems). Insome cases, even less accuracy is sufficient, for instance in people detection within aroom.Cost is a critical issue, particularly in applications where numerous units have to beequipped with localization technology, such as ad hoc sensor networks. It is thereforereasonable to use inexpensive standard off-the-shelf components whenever possible.Besides the mobile targets, infrastructure, deployment and maintenance cost must beconsidered.Power consumption must be considered, especially when a large number of batterydriven mobile units are involved (e.g. mobile phones).Another point is scalability. On the one hand, the system’s outer borders must be easilyextendable (e.g. by adding further rooms to the covered area). On the other hand, thesystem must be able to cope with an increasing number of mobile targets that need belocated concurrently.The estimation cycle length, i.e., the time between two location updates, must beconsidered in some cases. For instance, autonomous robots will act blindly until the nextposition update. If the update cycle is too long, they can only operate reliably at a verylow speed in order to avoid collisions.Robustness is another aspect that need be mentioned. This includes the level ofimmunity against interference from other sources, but also from ambient variations suchas temperature and humidity etc.Particularly in those systems where active tags are carried around by humans, device sizemust be taken into account. If these are large and heavy, the positioning system might beturned down by its users for concerns of convenience.17
At last, the utilized technology must be compatible with human beings that work or livewithin range of the localization system. For instance, acoustic range measurement usingartificial sound sources is unacceptable. Many domestic animals can perceive sound atfrequencies much higher than humans. Hence they might be affected by systems usingultrasound.18
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: with co-ordinates in the form of la
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 and 36: correlation between distance and si
Page 37 and 38: thus increasing the storage and mem
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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