A Thumb Tip Wearable Device Consisting of Multiple Cameras to Measure Thumb Posture

Naoto Ienaga, Wataru Kawai, Koji Fujita, Natsuki Miyata, Yuta Sugiura, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Today, cameras have become smaller and cheaper and can be utilized in various scenes. We took advantage of that to develop a thumb tip wearable device to estimate joint angles of a thumb as measuring human finger postures is important in terms of human-computer interface and to analyze human behavior. The device we developed consists of three small cameras attached at different angles so the cameras can capture the four fingers. We assumed that the appearance of the four fingers would change depending on the joint angles of the thumb. We made a convolutional neural network learn a regression relationship between the joint angles of the thumb and the images taken by the cameras. In this paper, we captured the keypoint positions of the thumb with a USB sensor device and calculated the joint angles to construct a dataset. The root mean squared error of the test data was 6.23° and 4.75° .

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers
EditorsGustavo Carneiro, Shaodi You
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030210731
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2018 Dec 22018 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018


  • Human computer interaction
  • Pose estimation
  • Wearable device

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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