Recognition of Grip-Patterns by Using Capacitive Touch Sensors

Chang, Wook; Kim, Kee Eung; Lee, Hyun-Jeong; Cho, Joon Kee; Soh, Byung Seok; Shim, Jung Hyun; Yang, Gyunghye; Cho, Sung-Jung; Park, Joonah

doi:10.1109/isie.2006.296083

Cited by 19 publications

(10 citation statements)

References 14 publications

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“…They showed that every digit could apply pressure accurately, but not equally. While not providing active input to the system, capacitive sensors around the edges of a mobile device have been used to detect hand posture [1]. Back-ofdevice interaction [13] avoids occlusion issues but input is generally limited to one digit during one-handed use.…”

Section: Related Workmentioning

confidence: 99%

“…When holding a mobile device one-handed, all five digits may be in contact around its edges, so there is an opportunity to provide multiple inputs from several digits simultaneously. Capacitive sensing around the device can be used to detect the location of digits and be used to change the current mode or function [1]. However, capacitive input is generally limited to binary input: either contact is made or it is not.…”

Section: Introductionmentioning

confidence: 99%

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Towards utilising one-handed multi-digit pressure input

Wilson

Brewster

Halvey

2013

CHI '13 Extended Abstracts on Human Factors in Computing Systems

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This paper explores the potential uses of pressure input from multiple digits (i.e., all 4 fingers and the thumb) of one hand squeezing a mobile device: multiple digits may provide multiple inputs. The potential advantages for mobile interaction include freeing the second hand for other tasks, and providing access to multiple functions simultaneously. A range of possible interactions is discussed including the benefits and challenges posed by complex pressure-based input on mobile devices. An example usage scenario is described and tested: pressure-based input for simultaneous zooming and rotating in a map task. Results suggest multi-digit pressure input may be a useful means of interaction with mobile devices

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards utilising one-handed multi-digit pressure input

Wilson

Brewster

Halvey

2013

CHI '13 Extended Abstracts on Human Factors in Computing Systems

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“…Back of device interaction also allows the creation of grasp-based technique that could predict users' intention by the way they hold the device. For instance, GripLaunch [2,5] is a tangible user interface that recognises a set of predefined hand grips and associated them with an application. For example grasping a mobile device horizontally with both hands can be associated with the camera or game applications.…”

Section: Related Workmentioning

confidence: 99%

Exploring back-of-device interaction

Noor¹

2013

Proceedings of the Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology - UIST '13 Ad

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Back of device interaction is gaining popularity as an alternative input modality in mobile devices, however it is still unclear how the back of device is related to other interactions. My research explores the relationship between hand grip from the back of the device and other interactions. In order to investigate this relationship, I will use touch target application to study hand grip patterns, then analyse the correlation that exists between touch target and hand grip. Finally I will explore the possibilities offered when the relationship between the touch target and hand grip is established in a quantifiable way.

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“…A parallel, independent study performed at the Samsung Advanced Institute of Technology (SAIT) provided further validation for this method of grasp recognition [2,3]. Using a different device geometry and set of device modes, they achieved similar recognition accuracies across a variety of classifiers.…”

Section: Related Workmentioning

confidence: 99%

The bar of soap

Taylor

Bove

2008

CHI '08 Extended Abstracts on Human Factors in Computing Systems

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We propose a vision of a grasp-based interaction system where users' intentions are inferred by the way they hold and interact with a device. In this paper we specifically discuss the Bar of Soap, a multi-function handheld prototype that uses grasp-based interactions to switch between modes. This prototype relies on the hypothesis that users share a set of stereotyped grasps associated with common multi-function modes. We show that using common machine learning techniques our device can reliably distinguish five separate classes based on the users' grasps. While this interaction is currently implemented in a multi-function handheld, we anticipate the existence of many scenarios where grasp recognition could provide a more intuitive or useful interface.

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Recognition of Grip-Patterns by Using Capacitive Touch Sensors

Cited by 19 publications

References 14 publications

Towards utilising one-handed multi-digit pressure input

Towards utilising one-handed multi-digit pressure input

Exploring back-of-device interaction

The bar of soap

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