Optimal hand sign selection using information theory for custom sign-based communication

Tokio Takahashi, Masato Uchida

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


Improving the communication abilities of people suffering from speech disorders or hearing impairments and who are struggling to learn sign or spoken language can improve their quality of life. However, methods to assist such people are not varied, and those that consider the degree of physical disability usually fail to attend particular needs. Thus, it is necessary to provide various communication methods according to the characteristics of each physical disability. In this paper, we devise a customized hand sign recognition system according to the degree of physical disability, and propose a method to select a customized set of signs comprising specific hand motions that an individual can effortlessly perform. We consider the optimal set as that providing high reliability and efficiency to realize smooth communication and apply information theory towards their selection. That is, we consider hand sign recognition from myoelectric potentials elicited by finger movement as a communication channel. Then, the optimal hand sign set is determined considering the set with the maximum channel capacity, as it reflects the most reliable and efficient combination. Finally, experimental results obtained from three subjects verify that the proposed method can determine the optimal set of hand signs according to each subject and that increasing the available hand signs or choosing hand signs with high recognition rate do not necessarily contribute to the optimal set.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728126074
Publication statusPublished - 2019 Jul
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: 2019 Jul 152019 Jul 19

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/TerritoryUnited States


  • Channel capacity
  • Hand sign
  • Myoelectric signal
  • Wearable device

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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