تشخیص زبان اشاره هندی با استفاده از حسگرهای پوشیدنی و طبقه بندی چند برچسب
نکات برجسته
خلاصه
چکیده گرافیکی
کلید واژه ها
1. مقدمه
2. ادبیات مرتبط
3. پیشنهاد ISL
4. نتایج تجربی
5. نتیجه گیری
بیانیه مشارکت نویسندگی CRediT
اعلامیه منافع رقابتی
تصدیق
ضمیمه. مواد تکمیلی
داده های تحقیق
منابع
Highlights
Abstract
Graphical abstract
Keywords
1. Introduction
2. Related literature
3. Proposed ISL recognition
4. Experimental results
5. Conclusion
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgment
Appendix. Supplementary materials
Research Data
References
Abstract
Sign language recognition is often carried out using hierarchical classification approach to reduce complexity and enhance accuracy. In this paper, mutli-label classification is proposed for categorization of a sign based on its lexical attributes followed by final classification of the sign. Results are presented for classification of 100 isolated signs from the Indian sign language recorded using multiple surface electromyogram and inertial measurement units on both the forearms of 10 different signers. Signals from both the hands are processed in an integrated manner to identify static or dynamic state of the two hands. Moreover, symmetry in the motion of two hands is also utilized for sign categorization using novel features. In the classic tree-based categorization of signs, there is error propagation, which results in a classification error of 6.22%. Whereas in the proposed mutli-label classification approach, error propagation is avoided and the average classification error of 2.73% is observed.
1. Introduction
A language provides humans with a structured means to exchange information with each other. While languages like Hindi and English use verbal or written mode of communication, sign languages, on the other hand, involve the use of visual gestures and signs. People with hearing and speech disabilities can communicate more naturally in sign language as compared to verbal languages. However, since most people do not understand sign languages, there is often a communication barrier experienced by a person wishing to converse in sign language. The use of a human interpreter or written form of communication is not always convenient. According to the Census 2001 of the Ministry of Home Affairs [1], there are around 1.26 million deaf people and around 1.64 million people with speech disability in India, while there are only 250 certified sign language interpreters in India. An electronic, wearable sign language recognition system shall be very useful in reducing the communication barrier that exists between a signer and a non-signer. There are several challenges in designing such a system.