GestureWrist and GesturePad: unobtrusive wearable interaction devices
By J. Rekimoto. Proceedings Fifth International Symposium on Wearable Computers, 2001
499 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/1cd75efeb2077add138505028a7c7329191df9e1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Human-Computer Interaction
- Chris Harrison
- Shahram Izadi
- Otmar Hilliges
- Robert Xiao
- I. Oikonomidis
- Gierad Laput
- Yang Zhang
- J. Laviola
Citations in the corpus
- ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, background): “Approaches can be passive, such as electromyography [45, 46], where gestures are classified by measuring the electrical signals caused by muscle activation, or active [9, 41], as in Touché [47] and Tomo [55], where a signal is injected into the body to detect hand gestures.”
- Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum (2016, unknown): “Finally, capacitive effects can be used for input recognition (e.g., [28]).”
- Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor (2012, background): “Given their lightweight form-factor, a variety of on-body placements including forearm [34] and wrist-worn [18, 19, 32] have been demonstrated.”
- Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition (2015, methodology): “For example, [18] used two gestures, while [17] used four.”