WatchSense

Sridhar, Srinath; Markussen, Anders; Oulasvirta, Antti; Theobalt, Christian; Boring, Sebastian

doi:10.1145/3025453.3026005

Cited by 65 publications

(15 citation statements)

References 42 publications

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“…For instance, it has been demonstrated that skin-based input [29] (tap on the skin) evokes higher SoA in users compared with typical keyboard-based input [17]. This finding may support application in skin-interaction smartwatches [70,79]. In other hand, speech input has been suggested to diminish SoA [45], which can provide major benefit in interface design.…”

Section: Application Of Agency Measures In Hci and Vrmentioning

confidence: 99%

Beyond the Libet Clock

Cornelio

Maggioni

Hornbæk

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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The Sense of Agency (SoA) refers to our capability to control our own actions and influence the world around us. Recent research in HCI has been exploring SoA to provide users an instinctive sense of "I did that" as opposed to "the system did that". However, current agency measurements are limited. The Intentional Binding (IB) paradigm provides an implicit measure of the SoA. However, it is constrained by requiring high visual attention to a "Libet clock" onscreen. In this paper, we extend the timing stimulus through auditory and tactile cues. Our results demonstrate that audio timing through voice commands and haptic timing through tactile cues on the hand are alternative techniques to measure the SoA using the IB paradigm. They both address limitations of the traditional method (e.g., lack of engagement and visual demand). We discuss how our results can be applied to measure SoA in tasks involving different interactive scenarios common in HCI.

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Section: Application Of Agency Measures In Hci and Vrmentioning

confidence: 99%

Beyond the Libet Clock

Cornelio

Maggioni

Hornbæk

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…Digits [11] requires an IR laser line projector whereas DigiTap [18] requires a LED flash synced with an accelerometer to detect vibrations occurring during finger taps. In WatchSense [24], the authors created a compact wearable prototype, attached to a user's forearm, to detect finger interaction from the other hand. Closest to our work, Chen et al [3] use elevated camera on the outer side of wrist to track 10 ASL hand poses.…”

Section: Vision Based Approaches On Wearable Devicesmentioning

confidence: 99%

“…Namely, when the hand intersects with another hand, or object, there are a range of interactions including, finger movement, touch, pinching or grasping. Consider, for example, in hand to hand interaction, the opisthenar area can act as a touchpad operated by the fingers of a second hand [24]. Such interactions can be extended to explore finger to finger interactions (such as clasping), or pinching or natural two-handed grasping actions.…”

Section: Future Workmentioning

confidence: 99%

Opisthenar

Yeo

Lee

et al. 2019

Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

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We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.

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“…Depth cameras [4,12,14] have usually been employed to create such surfaces, while other work have combined different sensor sources [13]. However, the touch classification, critical to the quality of the interaction remains challenging [14] and is traditionally addressed by hand-tuning parameters and thresholding.…”

Section: Introductionmentioning

confidence: 99%

Gesture Typing on Virtual Tabletop

Loriette

Murray-Smith

Stein

et al. 2017

Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces

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The association of tabletop interaction with gesture typing presents interaction potential for situationally or physically impaired users. In this work, we use depth cameras to create touch surfaces on regular tabletops. We describe our prototype system and report on a supervised learning approach to fingertips touch classification. We follow with a gesture typing study that compares our system with a control tablet scenario and explore the influence of input size and aspect ratio of the virtual surface on the text input performance. We show that novice users perform with the same error rate at half the input rate with our system as compared to the control condition, that an input size between A5 and A4 present the best tradeoff between performance and user preference and that users' indirect tracking ability seems to be the overall performance limiting factor.

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WatchSense

Cited by 65 publications

References 42 publications

Beyond the Libet Clock

Beyond the Libet Clock

Opisthenar

Gesture Typing on Virtual Tabletop

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