6 accepted papers
Mobility impairments, particularly those caused by stroke-induced hemiparesis, significantly impact independence and quality of life. Current smart walker controllers operate by using input forces from the user to control linear motion and input torques to dictate rotational movement; however, becau
Recent applications of deep reinforcement learning (DRL) in surgical autonomy have shown promising results in automating various surgical sub-tasks. While most of these studies consider the rigid and soft body dynamics in the surgery such as tissue deformation, only a few have investigated the situa
Gait asymmetry, a consequence of various neurological or physical conditions such as aging and stroke, detrimentally impacts bipedal locomotion, causing biomechanical alterations, increasing the risk of falls and reducing quality of life. Addressing this critical issue, this paper introduces a novel…
In this study, we present an intuitive machine learning-based approach to evaluate and interpret surgical skills level of a participant working with robotic platforms. The proposed method is domain-adapted, i.e., jointly utilizes an end-to-end learning approach for smoothness detection and domain kn
During the COVID-19 pandemic, due to the unprecedented workload and cross-infection hazard, the health-care workers’ lives are under a significant threat. However, minimizing the duration and frequency of close clinician-to-patient contacts using simple technologies that enable physical distancing c…
Due to the high sensitivity and complexity of robotic surgery tasks, acquiring appropriate skill levels by trainee surgeons through an effective training process is very important and affects the patient’s safety and the quality of surgical outcomes. With the advanced deep learning technology and th…