A Cooperative Suturing Strategy Based on AI and Multi-Source Information HMI

Currently, the rapid development of artificial in-telligence (AI) provides a sufficient technological basis for the development of autonomous medical robots, and the implemen-tation of autonomous control can produce more stable surgical results while reducing the surgeon's workload. Meanwhile, human-machine interface (HMI) has received much research attention due to its intuitive and natural control, and HMI has been widely used in medical rehabilitation and other fields. In order to fully combine the advantages of AI and HMI, reduce the surgeon's burden, and decrease the difficulty of surgical uptake, this paper proposes a cooperative suturing scheme based on AI and multi-source information HMI. On one side, the computer vision recognises the wound information and controls the robot to complete the automatic needle insertion operation, and on the other side, the human operator performs the needle extraction operation using the multi-source infor-mation HMI. We conducted 10 automatic needle insertion and extraction experiments using the cooperative suturing platform. The average error in the horizontal direction of the insertion point is 1.552mm and the average error in the horizontal direction of the extraction point is 2.011mm. The average completion time is 70.81s. The results show that the cooperative suturing platform has the ability to assist in-line continuous suturing.