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Neuronavigation is a computer-assisted technique for presenting three-dimensional images of a patient's brain to facilitate immediate and precise lesion localization by surgeons. Neuronavigation systems use preoperative medical images of patients. In neurosurgery, when the dura mater and arachnoid membrane are incised and the cerebrospinal fluid (CSF) drains out, the brain loses the CSF buoyancy and deforms in the direction of gravity, which is referred to as brain shift. This brain shift yields inaccurate neuronavigation. To reduce this inaccuracy, an intraoperative brain shift should be estimated. This paper proposes a dynamic simulation method for brain-shift estimation combining the moving-particle semi-implicit (MPS) method and the finite element method (FEM). The CSF was modeled using fluid particles, whereas the brain parenchyma was modeled using finite elements (FEs). Node particles were attached to the surface nodes of the brain parenchyma in the FE model. The interaction between the CSF and brain parenchyma was simulated using the repulsive force between the fluid particles and node particles. Validation experiments were performed using a gelatin block. The gelatin block was dipped into silicone oil, which was then gradually removed; the block deformation owing to the buoyancy loss was measured. The experimental deformation data were compared with the results of the MPS-FEM coupled analysis. The mean absolute error (MAE) between the simulated deformation and the average across the four experiments was 0.26 mm, while the mean absolute percentage error (MAPE) was 27.7%. Brain-shift simulations were performed using the MPS-FEM coupled analysis, and the computational cost was evaluated.

Laparoscopic surgery is a minimally invasive procedure that is performed by viewing endoscopic camera images. However, the limited field of view of endoscopic cameras makes laparoscopic surgery difficult. To provide more visual information during laparoscopic surgeries, augmented reality (AR) surgical navigation systems have been developed to visualize the positional relationship between the surgical field and organs based on preoperative medical images of a patient. However, since earlier studies used preoperative medical images, the navigation became inaccurate as the surgery progressed because the organs were displaced and deformed during surgery. To solve this problem, we propose a mixed reality (MR) surgery navigation system in which surgical instruments are tracked by a motion capture (Mocap) system; we also evaluated the contact between the instruments and organs and simulated and visualized the deformation of the organ caused by the contact. This paper describes a method for the numerical calculation of the deformation of a soft body. Then, the basic technology of MR and projection mapping is presented for MR surgical navigation. The accuracy of the simulated and visualized deformations is evaluated through basic experiments using a soft rectangular cuboid object.

In this study, we propose a method for measuring the distance and velocity of overlapping vehicles using high-speed stereo vision. The method is based on the correlation filter called MOSSE and is improved by balancing the computation cost and accuracy using the features of high-frame-rate images. To achieve stable tracking, the tracking window was adjusted for each vehicle according to the vehicle distance and the overlapping state. Experiments on the images acquired by the cameras mounted on an actual vehicle were performed to validate the proposed method.

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 ($${\mathrm{MAE}}_{\mathrm{median}}=2.2352$$ MAE median = 2.2352 ), and PCA-SVR in the parenchymal-suturing task ($${\mathrm{MAE}}_{\mathrm{median}}=1.2714$$ 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.

Kinect has been utilized as a cost-effective, easy-touse motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks' positions restrict Kinect's capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion's kinematic parameters were calculated using the landmarks' positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin's concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460 degrees) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843 degrees).

本研究では視線計測に基づく教育評価項目や評価指標の抽出を目的とし、気管内吸引シミュレーション中の看護師と看護学生を対象に両眼構成視線計測装置(Pupil，Pupil Labs社)を用いた視線計測により手技の所要時間、視線の停留回数・停留時間、停留点移動速度を比較した。視線計測後には、NASA-TLXを用いて作業負担度を調査した。その結果、所要時間、視線の停留時間、視線の停留回数・停留時間に有意差が認められた。作業負担度には有意差が見られなかったことから、両群とも模擬環境において同等のストレスがあったと推測される。不慣れな環境においてもパフォーマンスに差が現れることから、手技の所要時間や視線の停留回数・停留時間は習熟度を評価する客観的な指標となりうることが示された。

A method to achieve symmetric kinetostatic behaviour in an extensive working range at the endpoint of an asymmetric spatial beam, using cross-section optimization, is presented. The objective function of the optimization is defined as expanding the beam working range to the desired region, simultaneously maximizing symmetric behaviour in it. To reach this goal, a beam with predefined spatial global shape and an ‘I’ cross-section selected. The cross-sectional dimensions throughout the beam are used as input values for the optimization. The endpoint displacements under symmetric loadings are attained using a nonlinear co-rotational beam element based on the Euler-Bernoulli beam formulation. The optimized beams are compared to a circular cross-section beam with the same global shape to show the efficacy of the method. Isoforce diagrams are investigated for the optimized beams to show the symmetry behaviour of the beam endpoint and the effect of changing different parameters in cross-sectional optimization is discussed.

背景 : 在宅医療の高度化により, 気管内吸引等の高度ケアを担う人材の育成が喫緊の課題である.方法 : 気管内吸引を行う看護師と看護学生の眼球運動を測定し, 気管内吸引中の認知・判断面を含めた多重タスク構造を気管内吸引オントロジーとして可視化した.結果 : 構築された気管内吸引オントロジーの階層構造からは, 手順という手続き的知識のみならず, 医療機器に関する知識, 気管内吸引が無効であった際の選択肢に関する知識など, 多岐にわたった専門知識が要求される行為であることが示された.考察 : オントロジーは「できる」能力の範囲や評価すべき能力の定義を示す教育評価ツールとしても活用の可能性がある表現法と考えられた., Introduction: Due to the advancement of the home-based healthcare, increasing the number of professionals who are able to administer homebased treatments, such as endotracheal suctioning, is an urgent issue.Methods: By measuring the ocular movement of nurses, nursing students and previous researches, an endotracheal suctioning ontology was visualized. It included a multiplex task structure, recognition, and judgment, as well as identifying procedures used during endotracheal suctioning that could contribute to a discussion on educational feasibility.Results: From the hierarchical structure of the constructed endotracheal suctioning ontology, not only the procedural knowledge but also knowledge about medical devices, knowing what options exist when endotracheal suctioning is ineffective, and a wide range of other expertise is needed to perform endotracheal suctioning safely.Discussion: An endotracheal suctioning ontology was created from the ocular movement of nurses. Ontologies are a potential tool for defining minimum requirements and the scope of ability that must be evaluated before clinical practicum.

平成29年度衝撃波シンポジウム (2018年3月7日-9日. 東北大学片平さくらホール ）仙台市, 宮城県, (2018年3月7日-9日. 宇宙航空研究開発機構角田宇宙センター ）角田市, 宮城県, Symposium on Shock Waves in Japan, FY2017 (March 7-9, 2018. Sakura Hall), Sendai, Miyagi, (March 7-9, 2018. Kakuda Space Center, Japan Aerospace Exploration Agency (JAXA)(KSPC)), Kakuda, Miyagi, 著者人数: 15名, 資料番号: SA6000119002, レポート番号: 特別講演会 Ⅱ SP2

第3回EFD/CFD融合ワークショップ (2010年1月25日 秋葉原コンベンションホール) 千代田区, 東京, The 3rd Workshop on Integration of EFD and CFD (January 25, 2010. AKIHABARA Convention Hall) Chiyoda-ku, Tokyo, Japan, 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: AA0064721003, レポート番号: JAXA-SP-10-002

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