Motion Gesture Delimiters for Smartwatch Interaction

被引:2
|
作者
Zhao, Yiming [1 ]
Zhao, Yanchao [1 ]
Tu, Huawei [2 ]
Huang, Qihan [1 ]
Zhao, Wenlai [1 ]
Jiang, Wenhao [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 211100, Jiangsu, Peoples R China
[2] La Trobe Univ, Dept Comp Sci & Informat Technol, Melbourne, Vic 3086, Australia
基金
中国国家自然科学基金;
关键词
36;
D O I
10.1155/2022/6879206
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Research on Natural Gesture Interaction of Smartwatch
    Fu Jiuqiang
    Bing, Jiang
    Xin, Yang
    2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 2, 2016, : 566 - 569
  • [2] Finger gesture recognition using a smartwatch with integrated motion sensors
    Li, Yande
    Yang, Ning
    Li, Lian
    Liu, Li
    Yang, Yi
    WEB INTELLIGENCE, 2018, 16 (02) : 123 - 129
  • [3] Leveraging Smartwatch and Earbuds Gesture Capture to Support Wearable Interaction
    Rateau H.
    Lank E.
    Liu Z.
    Proceedings of the ACM on Human-Computer Interaction, 2022, 6 (ISS)
  • [4] DoubleFlip: A Motion Gesture Delimiter for Mobile Interaction
    Ruiz, Jaime
    Li, Yang
    29TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2011, : 2717 - 2720
  • [5] Exploring Gesture-Based Interaction in Smartwatch Games: A Comparative Study Between Continuous Gesture Recognition and Hidden Markov Models
    Silva, Leonardo
    Fernandes, Deborah
    Nogueira, Emilia
    Felix, Juliana
    Cardoso, Luciana
    Aranha, Renan
    Nascimento, Thamer Horbylon
    Soares, Fabrizzio
    ADVANCES IN VISUAL COMPUTING, ISVC 2024, PT II, 2025, 15047 : 448 - 459
  • [6] Demo: Smartwatch based Shopping Gesture Recognition
    Radhakrishnan, Meera
    Eswaran, Sharanya
    Sen, Sougata
    Subbaraju, Vigneswaran
    Misra, Archan
    Balan, Rajesh Krishna
    MOBISYS'16: COMPANION COMPANION PUBLICATION OF THE 14TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS, APPLICATIONS, AND SERVICES, 2016, : 115 - 115
  • [7] GESTURE AND EMBODIED INTERACTION: CAPTURING MOTION DATA VALUE
    Norman, Sally Jane
    Blackwell, Alan F.
    Warren, Lorraine
    Woolford, Kirk
    LEONARDO, 2010, 43 (02) : 198 - 199
  • [8] Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch
    Xu, Chao
    Pathak, Parth H.
    Mohapatra, Prasant
    16TH INTERNATIONAL WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS (HOTMOBILE' 15), 2015, : 9 - 14
  • [9] Pointing at the HUD: Gesture Interaction Using a Leap Motion
    Brand, Daniel
    Meschtscherjakov, Alexander
    Buechele, Kevin
    AUTOMOTIVEUI 2016: 8TH INTERNATIONAL CONFERENCE ON AUTOMOTIVE USER INTERFACES AND INTERACTIVE VEHICULAR APPLICATIONS, 2016, : 167 - 172
  • [10] SmartGe: Identifying Pen-Holding Gesture With Smartwatch
    Bi, Hongliang
    Zhang, Jian
    Chen, Yanjiao
    IEEE ACCESS, 2020, 8 (08): : 28820 - 28830