This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

Abstract
1. 서 론
2. 원형 구조 알고리즘 제안
    2.1 원형 구조 기반 데이터 수집
    2.2 기계학습 알고리즘
3. 실험 및 결과
    3.1 실험 대상 및 학습 환경
    3.2 실험 결과
4. 결 론
References

• 최유나(Department of Electrical and Electronic Engineering, Hanyang University) | Yuna Choi
• 성민창(Department of Electrical and Electronic Engineering, Hanyang University) | Minchang Sung
• 이슬아(Department of Electrical and Electronic Engineering, Hanyang University) | Seulah Lee
• 최영진(Department of Electrical and Electronic Engineering, Hanyang University) | Youngjin Choi Corresponding author