Minimally invasive surgery (MIS) is commonly used in pediatric operations. This method greatly benefits patients because of the reduced surgical trauma. To perform such surgery smoothly, doctors must be highly skilled. To reduce operating difficulties, a great deal of research on surgical systems have been carried out. However, in some cases, smaller workspaces limit the application of MIS. For example, the workspace of infant congenital esophageal atresia (ICEA) surgery is only around 30×30×30 mm. Until now, most ICEA surgeries have been manually performed with traditional instruments. This paper presents a smart surgical robot (SSR) for ICEA surgery. The robot is composed of two slave arms, each consisting of a positioning manipulator and a surgical tool manipulator. The positioning manipulator uses a selective compliance assembly robot arm (SCARA) and a screw-pair mechanism to achieve translational movement in 3D space, and the surgical tool manipulator uses a “double screw drive + universal joint” structure to allow an omni directional bending motion. During surgery, the surgeon first creates the workspace manually to explore the target esophagus. The SSR system is then applied to perform operation. The configuration of the SSR means it can perform tissue manipulation under endoscopic view in a small workspace. Experimental results show that the endoscopic view permits the SSR system to be operated intuitively and accurately in the target workspace.

Performing laparoscopic surgery requires several skills which have never been required for conventional open surgery, surgeons experience difficulties in learning and mastering these techniques. Various training methods and metrics have been developed in order to assess and improve surgeon's operative abilities. While these training metrics are currently widely being used, skill evaluation methods are still far from being objective in the regular laparoscopic skill education. This paper proposes a methodology of defining a model to objectively evaluate surgical performance and skill expertise in the routine laparoscopic training course. Our approach is based on the processing of kinematic data describing the movements of surgeon's upper limbs. An ultra-miniaturized wearable motion capture system (Waseda Bioinstrumentation system WB-3), therefore, has been developed to measure and analyze these movements. The skill evaluation model was trained by using the subjects' motion features acquired from WB-3 system and further validated to classify the expertise levels of the subjects with different laparoscopic experience. An experiment for training fundamental laparoscopic psychomotor skill was elaborated by using laparoscopic box trainer. Preliminary results show that, the proposed methodology can be efficiently used both for quantitative assessment of surgical ability, and for the discrimination between expert surgeons and novices.