Classification of User Postures with Capacitive Proximity Sensors in AAL-Environments

Große-Puppendahl, Tobias; Marinc, Alexander; Braun, Andreas

doi:10.1007/978-3-642-25167-2_43

Cited by 25 publications

(16 citation statements)

References 8 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%

“…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. In contrast to classification, models that determine continuous properties are often physically motivated.…”

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

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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: Classification and Continuous Object Trackingmentioning

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

“…In previous works, capacitive proximity sensors were applied in various fields of human-computer-interaction. Wimmer et al and Grosse-Puppendahl et al presented gesture recognition systems [25,10] as well as smart furniture that can sense human activities [25] and classify human postures [11]. Cheng et al have investigated the possibility of using capacitive sensors for activity recognition by measuring shape changes of muscles and skin [8].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Accelerometer-Based Activity Recognition with Capacitive Proximity Sensing

Große-Puppendahl

Berlin

Borazio

2012

Lecture Notes in Computer Science

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Abstract. Activity recognition with a wearable accelerometer is a common investigated research topic and enables the detection of basic activities like sitting, walking or standing. Recent work in this area adds different sensing modalities to the inertial data to collect more information of the user's environment to boost activity recognition for more challenging activities. This work presents a sensor prototype consisting of an accelerometer and a capacitive proximity sensor that senses the user's activities based on the combined sensor values. We show that our proposed approach of combining both modalities significantly improves the recognition rate for detecting activities of daily living.

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“…They have been applied to various applications for body parameter sensing in fields such as human-computer interaction or smart furniture, creating different interaction devices, or furniture that is able to sense occupation and posture [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

MoviBed - Sleep Analysis Using Capacitive Sensors

Djakow

Braun

Marinc

2014

Lecture Notes in Computer Science

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Abstract. Sleep disorders are a wide-spread phenomenon that can gravely affect personal health and well-being. An individual sleep analysis is a first step in identifying unusual sleeping patterns and providing suitable means for further therapy and preventing escalation of symptoms. Typically such an analysis is an intrusive method and requires the user to stay in a sleep laboratory. In this work we present a method for detecting sleep patterns based on invisibly installed capacitive proximity sensors integrated into the bed frame. These sensors work with weak electric fields and do not disturb sleep. Using the movements of the sleeping person we are able to provide a continuous analysis of different sleep phases. The method was tested in a prototypical setup over multiple nights.

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Classification of User Postures with Capacitive Proximity Sensors in AAL-Environments

Cited by 25 publications

References 8 publications

Finding Common Ground

Finding Common Ground

Enhancing Accelerometer-Based Activity Recognition with Capacitive Proximity Sensing

MoviBed - Sleep Analysis Using Capacitive Sensors

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