TY - JOUR
T1 - Enhanced sign language recognition using weighted intrinsic-mode entropy and signer's level of deafness
AU - Kosmidou, Vasiliki E.
AU - Petrantonakis, Panagiotis C.
AU - Hadjileontiadis, Leontios J.
N1 - Funding Information:
Manuscript received April 9, 2010; revised January 12, 2011; accepted April 30, 2011. Date of publication June 9, 2011; date of current version November 18, 2011. This work was supported by the General Secretariat for Research and Technology, Greek Ministry of Development, under Grant 44 (22195/16 December 2005) within the 3rd Community Support Programme–Operational Program “Information Society 2000–2008 A3-M3.3” (Funding: 75% European Regional Development Fund and 25% National Fund). This paper was recommended by Associate Editor V. Murino.
PY - 2011/12
Y1 - 2011/12
N2 - Sign language (SL) forms an important communication canal for the deaf. In this paper, enhanced SL recognition, by relating the individual way of signing with the signer's level of deafness (LoD) through a novel hybrid adaptive weighting (HAW) process applied to surface electromyogram and 3-D accelerometer data, is proposed. Using a LoD-driven genetic algorithm, HAW optimally weights the intrinsic modes of the acquired signals, preparing them for sample entropy (SampEn) estimation that follows. The resulting feature set, namely, weighted intrinsic-mode entropy (IMEn) (wIMEn), aims at increasing the SL-sign-classification accuracy alone or boosted by signer identification and/or signer's LoD-based group identification. The wIMEn was compared with three other feature sets, i.e., time frequency, SampEn, and IMEn, regarding their discrimination ability (both among signers and SL signs). Data from the dominant hand of nine subjects with various LoD were analyzed for the classification of 61 Greek SL (GSL) signs. Experimental results have shown that the introduced wIMEn feature set exhibited higher performance compared to others, both in signer identification and signer's LoD-based group identification and in GSL sign classification. The findings suggest that LoD could be considered in the construction of a signer-independent SL-classification system toward the enhancement of its performance.
AB - Sign language (SL) forms an important communication canal for the deaf. In this paper, enhanced SL recognition, by relating the individual way of signing with the signer's level of deafness (LoD) through a novel hybrid adaptive weighting (HAW) process applied to surface electromyogram and 3-D accelerometer data, is proposed. Using a LoD-driven genetic algorithm, HAW optimally weights the intrinsic modes of the acquired signals, preparing them for sample entropy (SampEn) estimation that follows. The resulting feature set, namely, weighted intrinsic-mode entropy (IMEn) (wIMEn), aims at increasing the SL-sign-classification accuracy alone or boosted by signer identification and/or signer's LoD-based group identification. The wIMEn was compared with three other feature sets, i.e., time frequency, SampEn, and IMEn, regarding their discrimination ability (both among signers and SL signs). Data from the dominant hand of nine subjects with various LoD were analyzed for the classification of 61 Greek SL (GSL) signs. Experimental results have shown that the introduced wIMEn feature set exhibited higher performance compared to others, both in signer identification and signer's LoD-based group identification and in GSL sign classification. The findings suggest that LoD could be considered in the construction of a signer-independent SL-classification system toward the enhancement of its performance.
UR - http://www.scopus.com/inward/record.url?scp=81955168078&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2011.2157141
DO - 10.1109/TSMCB.2011.2157141
M3 - Article
AN - SCOPUS:81955168078
SN - 1083-4419
VL - 41
SP - 1531
EP - 1543
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 6
M1 - 5872070
ER -