Using sample entropy for automated sign language recognition on sEMG and accelerometer data.
Communication using sign language (SL) provides alternative means for information transmission among the deaf. Automated gesture recognition involved in SL, however, could further expand this communication channel to the world of hearers. In this study, data from five-channel surface electromyogram...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 48; no. 3; pp. 255 - 268 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2010
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104908635&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104908635 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2010 vid: 48 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104908635 NLM19943194 2010564236 10.1007/s11517-009-0557-6 NLM19943194 104908635 ppf: 255 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Using sample entropy for automated sign language recognition on sEMG and accelerometer data. aug: au: Kosmidou VE Hadjileontiadis LI Kosmidou, Vasiliki E Hadjileontiadis, Leontios I affil: Department of Electrical & Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, University Campus, 541 24, Thessaloniki, Greece sug: subj: Information Science Methods Sign Language Motion Electromyography Methods Physics Female Body Language Male Signal Processing, Computer Assisted Female Male ab: Communication using sign language (SL) provides alternative means for information transmission among the deaf. Automated gesture recognition involved in SL, however, could further expand this communication channel to the world of hearers. In this study, data from five-channel surface electromyogram and three-dimensional accelerometer from signers' dominant hand were subjected to a feature extraction process. The latter consisted of sample entropy (SampEn)-based analysis, whereas time-frequency feature (TFF) analysis was also performed as a baseline method for the automated recognition of 60-word lexicon Greek SL (GSL) isolated signs. Experimental results have shown a 66 and 92% mean classification accuracy threshold using TFF and SampEn, respectively. These results justify the superiority of SampEn against conventional methods, such as TFF, to provide with high recognition hit-ratios, combined with feature vector dimension reduction, toward a fast and reliable automated GSL gesture recognition. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|