Background: The ability of nursing undergraduates to communicate effectively with health care providers, patients, and their family members is crucial to their nursing professions as these can affect patient outcomes. However, the traditional use of didactic lectures for communication skills training is ineffective, and the use of standardized patients is not time- or cost-effective. Given the abilities of virtual patients (VPs) to simulate interactive and authentic clinical scenarios in secured environments with unlimited training attempts, a virtual counseling application is an ideal platform for nursing students to hone their communication skills before their clinical postings. Objective: The aim of this study was to develop and test the use of VPs to better prepare nursing undergraduates for communicating with real-life patients, their family members, and other health care professionals during their clinical postings. Methods: The stages of the creation of VPs included preparation, design, and development, followed by a testing phase before the official implementation. An initial voice chatbot was trained using a natural language processing engine, Google Cloud’s Dialogflow, and was later visualized into a three-dimensional (3D) avatar form using Unity 3D. Results: The VPs included four case scenarios that were congruent with the nursing undergraduates’ semesters’ learning objectives: (1) assessing the pain experienced by a pregnant woman, (2) taking the history of a depressed patient, (3) escalating a bleeding episode of a postoperative patient to a physician, and (4) showing empathy to a stressed-out fellow final-year nursing student. Challenges arose in terms of content development, technological limitations, and expectations management, which can be resolved by contingency planning, open communication, constant program updates, refinement, and training. Conclusions: The creation of VPs to assist in nursing students’ communication skills training may provide authentic learning environments that enhance students’ perceived self-efficacy and confidence in effective communication skills. However, given the infancy stage of this project, further refinement and constant enhancements are needed to train the VPs to simulate real-life conversations before the official implementation.

Traditional rigid robotic arms have been widely used in various industries. They own the merits of high force output, precise control and high efficiency. However, a rigid robotic arm is seldom used by people in daily life, as the cost is usually high and, most importantly, its rigidity, large weight and non-compliance can cause safety concerns when it comes to human-robot interaction. Therefore, we want to build a robotic arm which is low-cost, light-weight and provides safe interaction with humans. This soft inflatable robotic arm, which is made from low-cost fabrics and flexible plastics, is a promising solution for a domestic robotic arm. The softness and natural compliance can guarantee safe interaction with people. In this paper, we present our soft robotic arm (SRA) made from nylon fabric. The SRA contains two joints which can bend the arm vertically and horizontally, and a gripper as an end effector, which can pick up objects of different shapes and sizes. The SRA has the advantages of low cost, good portability and adequate force output. We characterized the bending modality and force output and demonstrated the functionality of the SRA in object manipulation. This fabric-based soft robotic arm is promising for use in assisting people's daily activities, for example, mounting on a wheelchair to enable a wheelchair bound person to reach objects from a distance.

Minimally Invasive Surgery (MIS) is increasingly becoming a vital method of reducing surgical trauma and significantly improving post-operative recovery. However, skillful handling of surgical instruments used in MIS, especially for laparoscopy, requires a long period of training and depends highly on the experience of surgeons. This paper presents a new robot-assisted surgical training system which is designed to improve the practical skills of surgeons through intra-practice feedback and demonstration from both human experts and reinforcement learning (RL) agents. This system utilizes Proximal Policy Optimization (PPO) to learn the control policy in simulation. Subsequently, a Generative Adversarial Imitation Learning (GAIL) agent is trained based on both expert demonstrations and learned policies in simulation. This agent then generates demonstration policies on the robot-assisted device for trainees and produces feedback scores during practice. To further acquire surgical tools coordinates and encourage self-oriented practice, a Mask Region-based Convolution Neural Network (Mask R-CNN) is trained to perform the semantic segmentation of surgical tools and targets. To our best knowledge, this system is the first robot-assisted laparoscopy training system which utilizes actual surgical tools and leverages deep reinforcement learning to provide demonstration training from both human expert perspectives and RL criterion.