CapFloor – A Flexible Capacitive Indoor Localization System

Braun, Andreas; Heggen, H.; Wichert, Reiner

doi:10.1007/978-3-642-33533-4_3

Cited by 40 publications

(24 citation statements)

References 2 publications

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“…For example, in many capacitive indoor localization systems, a significant amount of instrumentation is needed underneath the floor [15,194,205]. Reusing parts of the environmental infrastructure for active sensing seems promising.…”

Section: Reducing Form Factor and Instrumentationmentioning

confidence: 99%

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Finding Common Ground

Große-Puppendahl

Holz

Cohn

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

164

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For more than two decades, capacitive sensing has played a prominent role in human-computer interaction research. Capacitive sensing has become ubiquitous on mobile, wearable, and stationary devices-enabling fundamentally new interaction techniques on, above, and around them. The research community has also enabled human position estimation and whole-body gestural interaction in instrumented environments. However, the broad field of capacitive sensing research has become fragmented by different approaches and terminology used across the various domains. This paper strives to unify the field by advocating consistent terminology and proposing a new taxonomy to classify capacitive sensing approaches. Our extensive survey provides an analysis and review of past research and identifies challenges for future work. We aim to create a common understanding within the field of human-computer interaction, for researchers and practitioners alike, and to stimulate and facilitate future research in capacitive sensing.

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Section: Reducing Form Factor and Instrumentationmentioning

confidence: 99%

“…Active sensing approaches for indoor localization commonly involve embedding large loading mode electrodes underneath floor tiles to determine on which tile the user is standing [15,57,194]. Alternatively, the entire floor can act as a single transmit electrode using transmit mode.…”

Section: Indoor Localizationmentioning

confidence: 99%

Finding Common Ground

Große-Puppendahl

Holz

Cohn

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

164

View full text Add to dashboard Cite

show abstract

“…Additionally, the data of the system can feed other subsystems in a Smart House. The work in [16] describes a flexible floor-based indoor localization. This system is based on capacitive sensing that is specifically designed to detect position and potential falls of users in a home environment.…”

Section: State Of the Artmentioning

confidence: 99%

In-home monitoring system based on WiFi fingerprints for ambient assisted living

Torres-Sospedra

Trilles

Calia

et al. 2017

AIS

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Abstract-This paper presents an in-home monitoring system based on WiFi fingerprints for Ambient Assisted Living. WiFi fingerprints are used to continuously locate a patient at the different rooms in her/his home. The experiments performed provide a correctly location rate of 96% in the best case of all studied scenarios. The behavior obtained by location monitoring allows to detect anomalous behavior such as long stays in rooms out of the common schedule. The main characteristics of our system are: a) it is robust enough to work without an own WiFi access point, which in turn means a very affordable solution; b) low obtrusiveness, as it is based on the use of a mobile phone; c) highly interoperable with other wireless connections (bluetooth, RFID) present in current mobile phones; d) alarms are triggered when any anomalous behavior is detected.

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“…The thesis presents a collection of existing and novel methods that support processing data generated by capacitive proximity sensors. These are in the areas of sparsely distributed sensors [3][4][5], model-driven fitting methods [3,6], heterogeneous sensor systems [7,8], image-based processing [8], and physiological signal processing [3,9]. To evaluate the feasibility of these methods, several prototypes have been created and tested for performance and usability.…”

Section: Thesis Summarymentioning

confidence: 99%