Faster Command Selection on Touchscreen Watches

Lafreniere, Benjamin; Gutwin, Carl; Cockburn, Andy; Grossman, Tovi

doi:10.1145/2858036.2858166

Cited by 35 publications

(17 citation statements)

References 22 publications

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“…Touchscreen-Driven Interaction. There are many studies that aim to use small touchscreens on wearable devices more efficiently such as Beats [4] and WristTap & TwoTap techniques [5]. Specifically, an area of interest for wearable computing researchers is to improve touchscreen-based soft keyboards for text entry.…”

Section: Tactile Interactionmentioning

confidence: 99%

A Non-Touchscreen Tactile Wearable Interface as an Alternative to Touchscreen-Based Wearable Devices

Yoon

Park

2020

Sensors

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Current consumer wearable devices such as smartwatches mostly rely on touchscreen-based user interfaces. Even though touch-based user interfaces help smartphone users quickly adapt to wearable devices with touchscreens, there exist several limitations. In this paper, we propose a non-touchscreen tactile wearable interface as an alternative to touchscreens on wearable devices. We designed and implemented a joystick-integrated smartwatch prototype to demonstrate our non-touchscreen tactile wearable interface. We iteratively improved and updated our prototype to improve and polish interaction ideas and prototype integration. To show feasibility of our approach, we compared and contrasted form factors of our prototype against the latest nine commercial smartwatches in terms of their dimensions. We also show response time and accuracy of our wearable interface to discuss our rationale for an alternative and usable wearable UI. With the proposed tactile wearable user interface, we believe our approach may serve as a cohesive single interaction device to enable various cross-device interaction scenarios and applications.

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Section: Tactile Interactionmentioning

confidence: 99%

A Non-Touchscreen Tactile Wearable Interface as an Alternative to Touchscreen-Based Wearable Devices

Yoon

Park

2020

Sensors

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“…Another way to aid gesture learning is to tap into spatial memory [23,35,47,48,49,19,39,7]. Spatial constancy allows users to rapidly locate target items when well practiced [13,12,43].…”

Section: Gesture Learningmentioning

confidence: 99%

M3 Gesture Menu

Zheng

et al. 2018

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

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More GPS Bluetooth Wifi Airplane (e) (d) (c) (f) Figure 1: M3 Gesture Menu prototyped on Android with two levels of 2 × 3 tiles. (a) M3 is initially hidden with a designated area (e.g. the white circle) presented as its activation button. (b) Pressing and holding the button for 0.5 seconds pops up the first-level menu. (c) Sliding the finger to an item (e.g. Settings) reveals its submenu which replaces the the higher level content in the same space. (d) Further sliding to a terminal node (e.g. Wi f i) activates its command. (e) Alternatively, experienced users may directly draw a gesture from the activation button approximately to Settings, then to Wi f i to trigger the same command. (f) Different gestures trigger different commands, e.g. the figure illustrates all the gestures in the Settings submenu.

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“…Finally, the spatial layout can leverage spatial memory as in CommandMaps [40] that flattens the hierarchy of commands to improve performance, or in FastTap [18] and its adaptation to smartwatches [28] which rely on a static grid that allows fast command selection on tablets and small devices.…”

Section: Related Workmentioning

confidence: 99%