| Title |
Wavelet Transform as Pre-processing for EMG-based Hand Gesture Recognition Suitable for Actual User-environment |
| Authors |
조용운(Yong-Un Jo) ; 오도창(Do-Chang Oh) |
| DOI |
https://doi.org/10.5573/ieie.2024.61.4.63 |
| Keywords |
Electromyograph; Hand gesture recognition; Actual user-environment Wavelet transform; TQWT |
| Abstract |
Hand gestures include not only grasping movements but also gestures for communication and using very important role in daily life. The field of hand gesture recognition using surface electromyography (sEMG) has been studied to recognize these hand movements and use them as HCI (Human Computer Interface). Since electromyography signals basically have many noise elements, various pre-processing techniques have been developed to recognize hand gestures, and Wavelet Transform (WT) is frequently used for frequency analysis. Therefore, in this paper, three pre-processing techniques based on wavelet transform are compared to select the most suitable technique for the actual user-environment in the field of hand gesture recognition. Processing time and gesture recognition accuracy in each technique were compared for real-time recognition, which is the main goal of the field of hand gesture recognition, and the compared wavelet transform techniques are DWT (Discrete WT), TQWT (Tunable Q-factor WT), and CWT (Continuous WT). The dataset used in the comparison contains 15 hand gestures collected from five different subjects, first extracting features with each of the three techniques and recognizing hand gestures using a deep learning classifier (light CNN). In each process in which hand gesture recognition is performed, processing time is recorded, and finally, the average accuracy for 15 hand gestures is calculated. The results obtained similar average accuracy of about 75% in TQWT and CWT, but the time required for the recognition process was 0.08 seconds in TQWT and 0.26 seconds in CWT. Therefore, it was shown that TQWT with fast processing time is a suitable technique for the actual user-environment of hand gesture recognition, showing similar performance in terms of accuracy. |