MoviBed - Sleep Analysis Using Capacitive Sensors

Djakow, Maxim; Braun, Andreas; Marinc, Alexander

doi:10.1007/978-3-319-07509-9_17

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…Due to its simplicity and ease of shielding [69,226], active loading mode is dominantly used for posture and touch recognition on furniture. Researchers have thereby augmented couches [69,110], bath tubs [89], beds [42], chairs [112,236], lamps [72], and tables [16,90,226] with capacitive sensing. Less conventional applications include fabric-based sensors inside bed sheets to enable low-power communication with wearable devices [68] or to recognize sleep postures [169].…”

Section: Instrumenting Everyday Objectsmentioning

confidence: 99%

“…Hardware for the shunt, transmit, and receive modes is similar and usually consists of an oscillator connected to the transmit electrode to generate an electric field, and an amplifier and analog-to-digital converter (ADC) connected to the receive electrode. In loading mode, [42,64,132,162] →Ink [59,57,58] →Paint [18] →Metalization [208] Gold -non-corrosive, skin-friendly →Gold leaf [98] →Gold coating [11] Aluminum -flexible, high availability →Foil [70,147,196,202] →Mylar [190] Silver -non-corrosive, flexible →Ink [76,87,105,108,203]+ →Thread/Yarn [185] →Plate [67] Carbon -enabling electrical conductivity →Ink [203] →Carbon-filled PDMS (cPDMS) [217] →Filament [20,123,177] ITO -transparent electrodes [64,119,199] Anisotropic materials -directing e-fields →Anisotropic 'Zebra' tape [91] →Anisotropic 'Zebra' rubber [24] Other materials →Water [41,173] & Plants [153] →Ionized gas [72] →Metal objects [43,68,173,207,226]+ →Electro-optic crystal [...…”

Section: Sensing Electronicsmentioning

confidence: 99%

“…Thresholding is ubiquitously applied to realize discrete classifiers, e.g., to passively sense user foot steps [163,197], touches [12], or presence [42,226]. If more than one state is being detected (e.g., multiple postures on a couch), classifiers, such as support vector machines [78,80,107,117] or decision trees [69,109,185,208] are frequently used.…”

Section: Classification and Continuous Object Trackingmentioning

confidence: 99%

See 2 more Smart Citations

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: Instrumenting Everyday Objectsmentioning

confidence: 99%

Section: Sensing Electronicsmentioning

confidence: 99%

Section: Classification and Continuous Object Trackingmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…The Smart Bed is a regular bed frame that has been equipped with capacitive proximity sensors, in order to determine occupation, posture and sleep phases [36], [65]. A sketch can be seen in Figure 14.…”

Section: Smart Bedmentioning

confidence: 99%

Capacitive proximity sensing in smart environments

Braun

Wichert

Kuijper

et al. 2015

AIS

Self Cite

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To create applications for smart environments we can select from a huge variety of sensors that measure environmental parameters or detect activities of different actors within the premises. Capacitive proximity sensors use weak electric fields to recognize conductive objects, such as the human body. They can be unobtrusively applied or even provide information when hidden from view. In the past years various research groups have used this sensor category to create singular applications in this domain. On the following pages we discuss the application of capacitive proximity sensors in smart environments, establishing a classification in comparison to other sensor technologies. We give a detailed overview of the background of this sensing technology and identify specific application domains. Based on existing systems from literature and a number of prototypes we have created in the past years we can specify benefits and limitations of this technology and give a set of guidelines to researchers that are considering this technology in their smart environment applications

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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%