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Background : Our aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments. Methods: Participants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5–25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model. Results: Forty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiency-related parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task ( MAE median = 2.2352 ), and PCA-SVR in the parenchymal-suturing task ( MAE median = 1.2714 ), based on 100 iterations of the validation process of automatic GOALS estimation. Conclusion: We developed a machine learning–based GOALS scoring system in wet lab training, with an error of approximately 1–2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program. [ABSTRACT FROM AUTHOR]

Background: Our aim was to characterize the motions of multiple laparoscopic surgical instruments among participants with different levels of surgical experience in a series of wet-lab training drills, in which participants need to perform a range of surgical procedures including grasping tissue, tissue traction and dissection, applying a Hem-o-lok clip, and suturing/knotting, and digitize the level of surgical competency. Methods: Participants performed tissue dissection around the aorta, dividing encountered vessels after applying a Hem-o-lok (Task 1), and renal parenchymal closure (Task 2: suturing, Task 3: suturing and knot-tying), using swine cadaveric organs placed in a box trainer under a motion capture (Mocap) system. Motion-related metrics were compared according to participants' level of surgical experience (experts: 50 ≤ laparoscopic surgeries, intermediates: 10–49, novices: 0–9), using the Kruskal–Wallis test, and significant metrics were subjected to principal component analysis (PCA). Results: A total of 15 experts, 12 intermediates, and 18 novices participated in the training. In Task 1, a shorter path length and faster velocity/acceleration/jerk were observed using both scissors and a Hem-o-lok applier in the experts, and Hem-o-lok-related metrics markedly contributed to the 1st principal component on PCA analysis, followed by scissors-related metrics. Higher-level skills including a shorter path length and faster velocity were observed in both hands of the experts also in tasks 2 and 3. Sub-analysis showed that, in experts with 100 ≤ cases, scissors moved more frequently in the "close zone (0 ≤ to < 2.0 cm from aorta)" than those with 50–99 cases. Conclusion: Our novel Mocap system recognized significant differences in several metrics in multiple instruments according to the level of surgical experience. "Applying a Hem-o-lok clip on a pedicle" strongly reflected the level of surgical experience, and zone-metrics may be a promising tool to assess surgical expertise. Our next challenge is to give completely objective feedback to trainees on-site in the wet-lab. [ABSTRACT FROM AUTHOR]

A quadrotor equipped with a fixed-wing (hereinafter referred to as the quadrotor tail-sitter UAV) has been developed by authors. The quadrotor tail-sitter UAV can hover like a quadrotor and can fly like a fixed-wing airplane. The remarkable characteristic of the developed quadrotor tail-sitter UAV is that it does not use any control surfaces during any form of flight; hovering, transition flight or level flight. This paper discusses an optimal transition from hovering to level flight for the quadrotor tail-sitter UAV because transition flight is most likely to be unstable throughout flight owing to stalling of the fixed-wing. The paper proposes three transition strategies: (i) normal transition (PID feedback to a step input), (ii) minimizing the transition time, and (iii) minimizing the transition time with keeping altitude constant. To realize the above optimal transition strategies, the aerodynamic parameters of the developed quadrotor tail-sitter UAV are measured by the wind tunnel test in advance. The three strategies are experimentally verified and the comparison among the three strategies is presented. [ABSTRACT FROM AUTHOR]

It is well known that the forward kinematics of parallel robots is a very difficult problem. Closed-form forward kinematics solutions have been reported only to a few special classes of parallel robots. This paper presents closed-form forward kinematics solutions of a 4-DOF parallel robot H4. A 16th order polynomial in a single variable is derived to solve the forward kinematics of the H4. The 16 roots of the polynomial lead to at most 16 different forward kinematics solutions. A numerical verification is also presented. [ABSTRACT FROM AUTHOR]

This article was updated to correct the labeling of Fig. 6. [ABSTRACT FROM AUTHOR]

This paper describes a hardware-in-the-loop simulation of massive-payload manipulation on orbit using a master-slave teleoperation system. The main problems in teleoperating a space robot arm from the earth to manipulate a massive payload are communication delay, unexpected excessive force generated between the slave arm and the payload, and geometric/dynamic modeling error. In order to overcome those problems, a teleoperation system using mixed force and motion commands is discussed. The teleoperation system is verified by performing hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]