Speech and gestures for graphic image manipulation
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Specifying gestures by example
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Evaluation of the CyberGlove as a whole-hand input device
ACM Transactions on Computer-Human Interaction (TOCHI)
Applying electric field sensing to human-computer interfaces
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
XWand: UI for intelligent spaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Low-cost multi-touch sensing through frustrated total internal reflection
Proceedings of the 18th annual ACM symposium on User interface software and technology
Thracker - Using Capacitive Sensing for Gesture Recognition
ICDCSW '06 Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems
The design of natural interaction
Multimedia Tools and Applications
Using the human body field as a medium for natural interaction
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Robust hand gesture recognition with kinect sensor
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Designing a multi-purpose capacitive proximity sensing input device
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Classification of user postures with capacitive proximity sensors in AAL-Environments
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
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Input devices based on arrays of capacitive proximity sensors allow the tracking of a user's hands in three dimensions. They can be hidden behind materials such as wood, wool or plastics without limiting their functionality, making them ideal for application in Ambient Intelligence (AmI) scenarios. Most gesture recognition frameworks are targeted towards classical input devices and interpret two-dimensional data. In this work, we present a concept for adapting classical gesture recognition methods for capacitive input devices by realizing an extension of the feature set to three dimensional input data. This allows more robust gesture recognition for free-space interaction and training specific to capacitive input devices. We have implemented this concept in a prototypical setup and tested the device in various Ambient Intelligence scenarios, ranging from manipulating home appliances to controlling multimedia applications.