Using Adaptive Directed Acyclic Graph for Human In-Hand Motion Identification with Hybrid Surface Electromyography and Kinect

• Published in: Symmetry (Basel) Vol. 14; no. 10; p. 2093
• Main Authors: Xue, Yaxu, Yu, Yadong, Yin, Kaiyang, Du, Haojie, Li, Pengfei, Dai, Kejie, Ju, Zhaojie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Bionics; Classification; Computer architecture; Data acquisition; Dextrous hands; Electromyography; End effectors; Hands; Human motion; Machine learning; Methods; Modal data; Motion perception; Recognition; Robotics; Robots; Sensors; Signal processing; Skills; Support vector machines; Task complexity
• DOI: 10.3390/sym14102093
• ISSN: 2073-8994

The multi-fingered dexterous robotic hand is increasingly used to achieve more complex and sophisticated human-like manipulation tasks on various occasions. This paper proposes a hybrid Surface Electromyography (SEMG) and Kinect-based human in-hand motion (HIM) capture system architecture for recognizing complex motions of the humans by observing the state information between an object and the human hand, then transferring the manipulation skills into bionic multi-fingered robotic hand realizing dexterous in-hand manipulation. First, an Adaptive Directed Acyclic Graph (ADAG) algorithm for recognizing HIMs is proposed and optimized based on the comparison of multi-class support vector machines; second, ten representative complex in-hand motions are demonstrated by ten subjects, and SEMG and Kinect signals are obtained based on a multi-modal data acquisition platform; then, combined with the proposed algorithm framework, a series of data preprocessing algorithms are realized. There is statistical symmetry in similar types of SEMG signals and images, and asymmetry in different types of SEMG signals and images. A detailed analysis and an in-depth discussion are given from the results of the ADAG recognizing HIMs, motion recognition rates of different perceptrons, motion recognition rates of different subjects, motion recognition rates of different multi-class SVM methods, and motion recognition rates of different machine learning methods. The results of this experiment confirm the feasibility of the proposed method, with a recognition rate of 95.10%.