TU Darmstadt / ULB / TUbiblio

Indoor Localization Based on Electric Potential Sensing

Dellangnol, Xavier :
Indoor Localization Based on Electric Potential Sensing.
Technische Universität Darmstadt , Darmstadt
[Masterarbeit], (2015)

Kurzbeschreibung (Abstract)

Indoor localization is needed in applications ranging from health care to entertainment. Although approaches based on video cameras have the upper hand in terms of accuracy and maturity, they raise privacy concerns and require heavy computation. Passive electric field sensing represents a low-cost, low-power, non-intrusive alternative for localization, which is investigated in this thesis. A human being naturally generates an electric field when walking. This field carries an am- biguous and nonlinear information about the person’s position. The present thesis proposes to combine measurements from several electric field sensors, thus resolving the ambiguity and obtaining a problem similar to trilateration. The method is presented as a detailed an- alytical model and implemented in a scalable system, the Platypus. The Platypus operates with a commercial sensor, the PS25451 EPIC (electric potential integrated circuit) manufac- tured by Plessey Semiconductors, which costs less than 10 € and consumes about 6mW. In this work, six sensors are fixed on the ceiling of a room, covering an area of 5m2, and the localization method is evaluated with 30 subjects. Results show that individuals walking at a comfortable speed are localized approximately twice each time they make a step, with an average error of 19.1 cm. The thesis contributes an original localization method that can be used in fusion with other systems, such as infrared sensors, to combine their respective strengths. The described analytical models have a large scope and can be adapted to other applications in human movement sensing.

Typ des Eintrags: Masterarbeit
Erschienen: 2015
Autor(en): Dellangnol, Xavier
Titel: Indoor Localization Based on Electric Potential Sensing
Sprache: Deutsch
Kurzbeschreibung (Abstract):

Indoor localization is needed in applications ranging from health care to entertainment. Although approaches based on video cameras have the upper hand in terms of accuracy and maturity, they raise privacy concerns and require heavy computation. Passive electric field sensing represents a low-cost, low-power, non-intrusive alternative for localization, which is investigated in this thesis. A human being naturally generates an electric field when walking. This field carries an am- biguous and nonlinear information about the person’s position. The present thesis proposes to combine measurements from several electric field sensors, thus resolving the ambiguity and obtaining a problem similar to trilateration. The method is presented as a detailed an- alytical model and implemented in a scalable system, the Platypus. The Platypus operates with a commercial sensor, the PS25451 EPIC (electric potential integrated circuit) manufac- tured by Plessey Semiconductors, which costs less than 10 € and consumes about 6mW. In this work, six sensors are fixed on the ceiling of a room, covering an area of 5m2, and the localization method is evaluated with 30 subjects. Results show that individuals walking at a comfortable speed are localized approximately twice each time they make a step, with an average error of 19.1 cm. The thesis contributes an original localization method that can be used in fusion with other systems, such as infrared sensors, to combine their respective strengths. The described analytical models have a large scope and can be adapted to other applications in human movement sensing.

Ort: Darmstadt
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Elektromechanische Konstruktionen
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Elektromechanische Konstruktionen > Mess- und Sensortechnik
Hinterlegungsdatum: 24 Apr 2017 15:09
Gutachter / Prüfer: Kupnik, Prof. Mario ; Große-Puppendahl, Dr.-Ing. Tobias ; Hatzfeld, Dr.-Ing. Christian
Export:

Optionen (nur für Redakteure)

Eintrag anzeigen Eintrag anzeigen