ABSTRACT
Over the last few years, contactless technology has gotten much attention because of the influence of the coronavirus (COVID-19). We focused on the hand written digits using contactless devices, Leap Motion Controller, for an information input method to accommodate the rapidly increasing demand. In this study, we verify the effectiveness of the proposed learning model that divides the subject's hand-tracking data according to the type of fingers and learn with multiple input layer. We set two kinds of evaluation methods. The normal recognition method is that specific subject data includes training and test data. The third party recognition method is that specific subject data does include training data. The classification accuracy calculated by the proposed learning model, the normal recognition method achieves a maximum of 96.7%, and the third party recognition method achieves a maximum of 80.1%. © 2023 IEEE.
ABSTRACT
Robotic interventional neuroradiology is an emerging field with the potential to enhance patient safety, reduce occupational hazards, and expand systems of care. Endovascular robots allow the operator to precisely control guidewires and catheters from a lead-shielded cockpit located several feet (or potentially hundreds of miles) from the patient. This has opened up the possibility of expanding telestroke networks to patients without access to life-saving procedures such as stroke thrombectomy and cerebral aneurysm occlusion by highly-experienced physicians. The prototype machines, first developed in the early 2000s, have evolved into machines capable of a broad range of techniques, while incorporating newly automated maneuvers and safety algorithms. In recent years, preliminary clinical research has been published demonstrating the safety and feasibility of the technology in cerebral angiography and intracranial intervention. The next step is to conduct larger, multisite, prospective studies to assess generalizability and, ultimately, improve patient outcomes in neurovascular disease.