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Recognition of Bed Postures Using Mutual Capacitance Sensing

: Rus, Silvia; Große-Puppendahl, Tobias; Kuijper, Arjan


Aarts, Emile (Ed.); Ruyter, Boris de (Ed.); Markopoulos, Panos (Ed.); Loenen, Evert van (Ed.); Wichert, Reiner (Ed.); Schouten, Ben (Ed.); Terken, Jacques (Ed.); Kranenburg, Rob van (Ed.); Ouden, Elke Den (Ed.); O'Hare, Gregory (Ed.):
Ambient intelligence. European Conference, AmI 2014 : Eindhoven, The Netherlands, November 11 - 13, 2014; Revised selected papers
Cham: Springer International Publishing, 2014 (Lecture Notes in Computer Science 8850)
ISBN: 978-3-319-14111-4 (Print)
ISBN: 978-3-319-14112-1 (Online)
DOI: 10.1007/978-3-319-14112-1
European Conference on Ambient Intelligence (AmI) <11, 2014, Eindhoven>
Fraunhofer IGD ()
Activity recognition; Capacitive sensors; Ambient assisted living (AAL); Business Field: Digital society; Research Area: Human computer interaction (HCI)

In recent years, mutual capacitive sensing made significant advances in the field of gathering implicit contextual data. These systems find broad usage in pervasive activity-recognition systems, installed stationary or made portable. In the domain of context recognition new ways of interaction with the environment opened up since conductive objects can be detected under certain conditions at distances up to 50 cm.
This paper investigates an approach to recognize bed postures using mutual capacitance sensing. The overall goal is to develop a technological concept that can be applied to recognize bed postures of patients in elderly homes. The use of this contextual data may lead to many desired benefits in elderly care e.g. the better prevention of decubitus ulcer, a condition caused by prolonged pressure on the skin resulting in injuries to skin and underlying tissues. For this, we propose a low-cost grid of crossed wires of 48 measurement points placed between the mattress and the bed sheet. The experimental results analyze a set of five lying positions. We achieved for all tested individuals an accuracy of 80.76% and for several individuals of the same bodysize an accuracy of 93.8%.