Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76. I. INTRODUCTIONRobot-assisted surgery (RAS) is now a standard procedure across various surgical specialties, including gynecology, urology and general surgeries. During the last two decades, over 2 million procedures were performed using the Intuitive Surgical's daVinci robot in the U.S. [1]. Surgical robots are complex human-in-the-loop Cyber-Physical Systems (CPS) that enable 3D visualization of surgical field and more precise manipulation of surgical instruments such as scissors, graspers, and electro-cautery inside patient's body. The current generation of surgical robots are not fully autonomous yet. They are in level 0 of autonomy [2], following the commands provided by the surgeons from a master-side teleoperation console in real-time (Figure 1a), translating them into precise movements of robotic arms and instruments, while scaling surgeon's motions and filtering out handtremors. By increasing flexibility and precision, surgical robots have enabled new types of surgical procedures and have reduced complication rates and procedure times.Recent studies have shown that safety in robotic surgery may be compromised by vulnerabilities of the surgical robots to accidental or maliciously-crafted faults in the cyber or physical layers of the control system or human errors [3], [4]. Examples of system faults include disruptions of the communication between the surgeon console and the robot, causing packet drops or delays in tele-operation [5], accidental or malicious faults targeting the robot control software [6],

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Healthcare cognitive assistants (HCAs) are intelligent systems or agents that interact with users in a context-aware and adaptive manner to improve their health outcomes by augmenting their cognitive abilities or complementing a cognitive impairment. They assist a wide variety of users ranging from patients to their healthcare providers (e.g., general practitioner, specialist, surgeon) in several situations (e.g., remote patient monitoring, emergency response, robotic surgery). While HCAs are critical to ensure personalized, scalable, and efficient healthcare, there exists a knowledge gap in finding the emerging trends, key challenges, design guidelines, and state-of-the-art technologies suitable for developing HCAs. This survey aims to bridge this gap for researchers from multiple domains, including but not limited to cyber-physical systems, artificial intelligence, human-computer interaction, robotics, and smart health. It provides a comprehensive definition of HCAs and outlines a novel, practical categorization of existing HCAs according to their target user role and the underlying application goals. This survey summarizes and assorts existing HCAs based on their characteristic features (i.e., interactive, context-aware, and adaptive) and enabling technological aspects (i.e., sensing, actuation, control, and computation). Finally, it identifies critical research questions and design recommendations to accelerate the development of the next generation of cognitive assistants for healthcare.

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