This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform ‘tying up shoe’ using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user’s intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

Abstract
1. 서 론
2. 다자유도 전완 의수 설계 및 제작
    2.1 다자유도 손 메커니즘 설계
    2.2 1자유도 손목 메커니즘 설계
    2.3 전완용 소켓 디자인
    2.4 패턴 인식 및 분류 알고리즘
3. 실험 방법 및 결과
    3.1 실험 대상
    3.2 실험 결과
4. 결 론
References

• 이슬아(Department of Electrical and Electronic Engineering, Hanyang University) | Seulah Lee
• 최유나(Department of Electrical and Electronic Engineering, Hanyang University) | Yuna Choi
• 양세동(Department of Electrical and Electronic Engineering, Hanyang University) | Sedong Yang
• 홍근영(Department of Electrical and Electronic Engineering, Hanyang University) | Geun Young Hong
• 최영진(Department of Electrical and Electronic Engineering, Hanyang University) | Youngjin Choi Corresponding author