Gestural Text Input Using a Smartwatch

Katsuragawa, Keiko; Wallace, James R.; Lank, Edward

doi:10.1145/2909132.2909273

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…Gesture interaction is especially suitable for mobile devices, such as changing the screen display direction by tilting the phone [29] and moving the cursor with gestures [30]. In addition, gesture interaction can also achieve more complex operations, such as text input with gestures on smartwatches [31], identity authentication by recognizing user gestures [32], and access data on virtual bookshelves around users [33]. An important step of using motion gestures is to separate normal smartwatch motion from a user's intended input.…”

Section: Related Workmentioning

confidence: 99%

Motion Gesture Delimiters for Smartwatch Interaction

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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Smartwatches are increasingly popular in our daily lives. Motion gestures are a common way of interacting with smartwatches, e.g., users can make a movement in the air with their arm wearing the watch to trigger a specific command of the smartwatch. Motion gesture interaction can compensate for the small screen size of the smartwatch to some extent and enrich smartwatch-based interactions. An important aspect of motion gesture interaction lies in how to determine the start and end of a motion gesture. This paper is aimed at selecting gestures as suitable delimiters for motion gesture interaction with the smartwatch. We designed six gestures (“shaking wrist left and right,” “shaking wrist up and down,” “holding fist and opening,” “turning wrist clockwise,” “turning wrist anticlockwise,” and “shaking wrist up”) and conducted two experiments to compare the performance of these six gestures. Firstly, we used dynamic time warping (DTW) and feature extraction with KNN ( K -nearest neighbors) to recognize these six gestures. The average recognition rate of the latter algorithm for the six gestures was higher than that of the former. And with the latter algorithm, the recognition rate for the first three of the six gestures was greater than 98%. According to experiment one, gesture 1 (shaking wrist left and right), gesture 2 (shaking wrist up and down), and gesture 3 (holding fist and opening) were selected as the candidate delimiters. In addition, we conducted a questionnaire data analysis and obtained the same conclusion. Then, we conducted the second experiment to investigate the performance of these three candidate gestures in daily scenes to obtain their misoperation rates. The misoperation rates of two candidate gestures (“shaking wrist left and right” and “shaking wrist up and down”) were approximately 0, which were significantly lower than that of the third candidate gesture. Based on the above experimental results, gestures “shaking wrist left and right” and “shaking wrist up and down” are suitable as motion gesture delimiters for smartwatch interaction.

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Section: Related Workmentioning

confidence: 99%

Motion Gesture Delimiters for Smartwatch Interaction

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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“…splitboard [19], Flexy or Swype [5]. An additional method of extending smartwatch interaction has been by exploring gesture-based input [23,48] -with varying levels of successbut by far the most utilised methodology is to simply extend the zone of interaction of a smartwatch by utilising the space on and around the arm, or by adding functionality and screen space to the smartwatch itself.…”

Section: Related Workmentioning

confidence: 99%

Foldwatch

Fuchs

Sturdee

Schöning

2018

Proceedings of the 10th Nordic Conference on Human-Computer Interaction

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Smartwatches are highly portable, ubiquitous devices, allowing rich interaction at a small scale. However, the display size can hinder user engagement, limit information display, and presentation style. Most research focuses on exploring ways in which the interaction area of smartwatches can be extended, although this mainly entails simple fold-out displays or additional screens. Conversely, added weight and size can hinder the wearable experience. In response, we took inspiration from origami and explored the design space for new types of lightweight, highly foldable smartwatch, by developing complex paper-prototypes which demonstrate novel ways of extending screen space. We collected data on potential input and output interaction with complex folded smartwatch displays during workshops with expert and non-expert users, discovering application ideas and additional input/output functionality. These insights were used to produce and evaluate a concept video for the FoldWatch prototype.

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“…Modern smartphones use tilt to change screen orientation, an innovation credited to Hinckley et al [15]. Additionally, motion input has also been used for a variety of other input tasks, such as text input [16,19,27,40], controlling a cursor [39], user verification [22], and accessing data on virtual shelves around a user [20].…”

Section: Designing Motion Gesture Inputmentioning

confidence: 99%

Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior

Katsuragawa

Kamal

Lank

2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

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Bi-level thresholding is a motion gesture recognition technique that mediates between false positives, and false negatives by using two threshold levels: a tighter threshold that limits false positives and recognition errors, and a looser threshold that prevents repeated errors (false negatives) by analyzing movements in sequence. In this paper, we examine the effects of bi-level thresholding on the workload and acceptance of endusers. Using a wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding. Given identical recognition rates, we show that systems using bi-level thresholding result in significant lower workload scores on the NASA-TLX and accelerometer variance. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors and false negatives.

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Gestural Text Input Using a Smartwatch

Cited by 13 publications

References 5 publications

Motion Gesture Delimiters for Smartwatch Interaction

Motion Gesture Delimiters for Smartwatch Interaction

Foldwatch

Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior

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