Earticle

초록

영어

The use of surface electromyography has increase recently for hand gesture recognition because of the feasible usage of low cost, wearable, non-invasive devices. Hand gesture enhances human-machine interaction to great extent. This paper proposed a robust approach for hand gesture classification using various machine learning classifiers. Six different features such as; minimum, maximum, peak to peak, root mean square, zero crossing and waveform length are extracted from raw data and fed to machine learning classifiers. Data is comprised of 36 individuals and seven gestures are classified with an accuracy of 90% and F1 score of 87% using Support Vector Machine classifier. Our reproducible implementation is available at github.com/talhaanwarch/emg-gesture-classification

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Dataset
B. Feature Extraction
C. Classification
D. Cross-Validation
E. Evaluation
III. RESULTS
IV. CONCLUSION
REFERENCES

저자

• Seemab Zakir [ Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur, Pakistan ]
• Talha Anwar [ Independent Researcher Multan, Pakistan ]
• Muhammad Waqas [ Dept of Computer Science and Technology Xi’an University of Science and Technology China ]
• Vaneeza Iman [ Department of Software Engineering Lahore Garrison University Lahore, Pakistan. ]
• Mubashir Ali [ Department of Software Engineering Lahore Garrison University Lahore, Pakistan ]

참고문헌