More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

The wearable electronics integrated with textile-based devices is a promising strategy to meet the requirements of human comfort as well as electrical performances. This research presents a design and development framework for a seamless glove sensor system using digital knitting fabrication. Based on the performance requirements of glove sensors for controlling a prosthetic hand, desirable design components include electrical conductivity, comfort, formfit, electrical sensitivity, and customizable design. These attributes are determined and achieved by applying appropriate materials and fabrication technologies. In this study, a digital knitting CAD/CAM system is utilized to meet the desired performance criteria, and two prototypes of the seamless glove sensor systems are successfully developed for the detection of both human and robotic finger motions. This digital knitting system will provide considerable potential for customized design development as well as a sustainable production process. This structured, systematic approach could be adapted in the future development of wearable electronic textile systems.

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