Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.
With the development of science and technology, artificial intelligence is gradually integrated into every aspect of daily life and the medical field is no exception. Cardiovascular diseases, as the first killer to global health, is the focus of new technologies and methods. In this study, the application of computer vision, natural language processing, robotics and machine learning in cardiovascular disease studies were reviewed and prospected, in order to promote the development for new technologies and applications in the future.
Objective To develop a novel recognition algorithm that can assist physicians in locating pulmonary nodules. MethodsSixteen patients with pulmonary nodules who underwent thoracoscopic surgery at the Department of Thoracic Surgery, the Affiliated Drum Tower Hospital of Nanjing University Medical School were enrolled, including 9 males and 7 females with an average age of (54.9±14.9) years. Chest surface exploration data of 60 frames per second and1 920×1 080 resolution were collected from patients, and frame images were saved at regular intervals for block processing. An algorithm database for lung nodule recognition using the above data was built. ResultsIn the optimized multi topology convolutional network model, the test results showed an accurate recognition rate of 94.39%. Furthermore, by integrating micro-variation amplification technology into the convolutional network model, the accuracy of identifying lung nodules was improved to 96.90%. By comprehensively evaluating the performance of these two models, the overall recognition accuracy reached 95.59%. Based on this, we infered that the proposed network model was suitable for the recognition task of lung nodules, and the convolutional network incorporating micro-variation amplification technology performs better in accuracy. Conclusion Compared with traditional methods, our proposed technique can significantly improve the accuracy of lung nodule identification and localization, and help surgeons locate lung nodules during thoracoscopic surgery.