title: Neural network application in Japanese sign language: distinction of similar Yubimoji gestures.
abstract: In a previous paper, the authors built a neural network model to recognize Japanese sign language syllabary or yubimoji. One of the problems encountered in that study was the accurate digital representation and distinction of similar yubimoji gestures, i.e. gestures with the same finger flexure positions but with different hand/finger orientations. This study focuses on these yubimoji gestures. Using data from a glove interface with bend sensors and accelerometers, a neural network was built, trained and tested. The network performed well and good results were obtained.
authors.affiliation: Kiryu University, Faculty of Health Care, Asami 606-7, Kasakake, Midori City, Gunma, Japan. machacon-htc@kiryu-u.ac.jp
authors.forename: H T C
authors.initials: HT
authors.lastname: Machacon
authors.forename: S
authors.initials: S
authors.lastname: Shiga
authors.forename: K
authors.initials: K
authors.lastname: Fukino
mesh_terms: D003143:Communication Devices for People with Disabilities; D005385:Fingers; D005868:Gestures; D006801:Humans; D007564:Japan; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012813:Sign Language; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D014825:Vocabulary