Abstract
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 language | English |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 |
| Editors | Vladimir 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 |
| Publisher | IEEE Computer Society |
| Pages | 610-615 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728126074 |
| DOIs | |
| Publication status | Published - 2019 Jul |
| Event | 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States Duration: 2019 Jul 15 → 2019 Jul 19 |
Publication series
| Name | Proceedings - International Computer Software and Applications Conference |
|---|---|
| Volume | 1 |
| ISSN (Print) | 0730-3157 |
Conference
| Conference | 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 |
|---|---|
| Country | United States |
| City | Milwaukee |
| Period | 19/7/15 → 19/7/19 |
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Keywords
- Channel capacity
- Hand sign
- Myoelectric signal
- Wearable device
ASJC Scopus subject areas
- Software
- Computer Science Applications
Cite this
Optimal hand sign selection using information theory for custom sign-based communication. / Takahashi, Tokio; Uchida, Masato.
Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. ed. / Vladimir 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. IEEE Computer Society, 2019. p. 610-615 8754297 (Proceedings - International Computer Software and Applications Conference; Vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Optimal hand sign selection using information theory for custom sign-based communication
AU - Takahashi, Tokio
AU - Uchida, Masato
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Channel capacity
KW - Hand sign
KW - Myoelectric signal
KW - Wearable device
UR - http://www.scopus.com/inward/record.url?scp=85072715663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072715663&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2019.00093
DO - 10.1109/COMPSAC.2019.00093
M3 - Conference contribution
AN - SCOPUS:85072715663
T3 - Proceedings - International Computer Software and Applications Conference
SP - 610
EP - 615
BT - Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
A2 - Getov, Vladimir
A2 - Gaudiot, Jean-Luc
A2 - Yamai, Nariyoshi
A2 - Cimato, Stelvio
A2 - Chang, Morris
A2 - Teranishi, Yuuichi
A2 - Yang, Ji-Jiang
A2 - Leong, Hong Va
A2 - Shahriar, Hossian
A2 - Takemoto, Michiharu
A2 - Towey, Dave
A2 - Takakura, Hiroki
A2 - Elci, Atilla
A2 - Takeuchi, Susumu
A2 - Puri, Satish
PB - IEEE Computer Society
ER -