The trend of achieving minimal invasiveness in surgeries and recent technological advances in robotics have resulted in the emergence of flexible surgical robots. Such flexible robots can reach a surgical site via narrow and tortuous pathways, extending the reach of robotic surgery and potentially reducing the incision size. This review covers the key technical issues associated with flexible surgical robotics and introduces emerging flexible surgical robot systems organized according to their target applications in the endoluminal surgical field. Furthermore, the challenges and recent advancements in manipulator design, modeling, and control as well as the shape and force sensing of flexible robots are presented as key technical issues. Furthermore, the technical features and clinical values of emerging flexible surgical robot systems are introduced with their medical applications.

Owing to the trend toward minimally invasive surgery, manipulators are in demand to achieve more accessibility, agility, and miniaturization. However, conventional manipulators are not flexible, miniaturized, or strong enough to perform surgical procedures in deep, confined spaces. Moreover, the continuum manipulator has additional problems caused by redundancy, such as shape-distortion, deflection, and wire over-tension problems occurring during payload handling. To overcome these problems, a novel constrained continuum (CC) manipulator is proposed. The CC constrains the redundancy mechanically by using auxiliary links attached to the main continuum links. Its main advantage consists of significantly enhancing the payload capability by the constraining mechanism and the increased length of the moment arm. The proposed manipulator was verified as capable of repeatedly handling a payload of up to 300 g without exceeding the expected trajectory. Two types of in vivo animal trials demonstrated the manipulator's performance regarding maneuverability, workspace, and payload, to successfully resect the tissue. This paper presents the kinematic/mechanical analysis results and proposes the optimization of the key parameters and foundational guidelines for the CC joint. We believe that the proposed manipulator advances the field of surgical robotics owing to its ability to overcome surgical procedures at a standstill.

This paper proposes an automatic fall detector in a wearable device that can reduce risks by detecting falls and promptly alerting caregivers. For this purpose, we propose cluster-analysis-based user-adaptive fall detection using a fusion of heart rate sensor and accelerometer. The objectives of the proposed fall detector are to have high accuracy with a low-complexity model regardless of diverse conditions. To meet the objectives, we propose the best 13-dimensional feature subset by using feature selection. In addition, we verify the performance increment of combining a heart rate sensor with an accelerometer and the effectiveness of the cluster-analysis-based anomaly detection. We also show the effectiveness of the user-adaptive method when using both heart rate and acceleration signals that were hardly covered in other papers. Finally, we prove that the performance of the proposed fall detector achieves is better than that of recent user-adaptive and user-independent approaches. This study is the first attempt to demonstrate the merits of the user-adaptive approach using a combination of heart rate and acceleration signals to detect falls. Moreover, this paper also contributes to fall detection area by providing the data we collected.

Tendon-sheath-driven manipulators (TSM) are widely used in minimally invasive surgical systems due to their long, thin shape, flexibility, and compliance making them easily steerable in narrow or tortuous environments. Many commercial TSM-based medical devices have non-linear phenomena resulting from their composition such as backlash hysteresis and dead zone, which lead to a considerable challenge for achieving precise control of the end effector pose. However, many recent works in the literature do not consider the combined effects and compensation of these phenomena, and less focus on practical ways to identify model parameters in realistic conditions. This paper proposes a simplified piecewise linear model to construct both backlash hysteresis and dead zone compensators together. Further, a practical method is introduced to identify model parameters using motor current from a robotic controller for the TSM. Our proposed methods are validated with multiple Intra-cardiac Echocardiography (ICE) catheters, which are typical commercial example of TSM, by periodic and non-periodic motions. Our results show that the errors from backlash hysteresis and dead zone are considerably reduced and therefore the accuracy of robotic control is improved when applying the presented methods.