In recent years, driving in an aimless state is the worst cause of fatal car accidents in Japan. The goal of our study is to detect the aimless state of car drivers noninvasively in order to prevent the fatal car accidents. Eye movements are highly possible biological indices that reflect the aimless state of car drivers as they are affected by various mental states such as sleepiness, and attentiveness. In this paper, we evaluated the movements of both eyes while subjects were performing a driving simulation (DS) and a simple reaction test. We demonstrate that the eyes fluctuate slowly without making saccades (SEM) and simultaneously the vergence angle rapidly fluctuates (FVA), when the car deviated from its lane during the DS and the subject took significantly long reaction time during the reaction test. Thus the cooccurrence of SEM and FVA can be a reliable objective measure of the aimless state even when the driver is looking ahead with his/her eyes open.

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To achieve 3D kinematic analysis of total knee arthroplasty (TKA), 2D/3D image registration techniques, which use X-ray fluoroscopic images and computer aided design (CAD) model of the knee implant, have attracted attention in recent years. These techniques could provide information regarding the movement of radiopaque femoral and tibial components but could not provide information of radiolucent polyethylene insert, because the insert silhouette on X-ray image did not appear clearly. Therefore, it was difficult to obtain 3D kinemaitcs of the polyethylene insert, particularly the mobile-bearing insert that move on the tibial component. This study presents a technique and the accuracy for 3D kinematic analysis of the mobile-bearing insert in TKA using X-ray fluoroscopy, and finally performs clinical applications. For a 3D pose estimation technique of the mobile-bearing insert in TKA using X-ray fluoroscopy, tantalum beads and CAD model with its beads are utilized, and the 3D pose of the insert model is estimated using a feature-based 2D/3D image registration technique. In order to validate the accuracy of the present technique, experiments including computer simulation test were performed. The results showed the pose estimation accuracy was sufficient for analyzing mobile-bearing TKA kinematics, and the estimation errors were within 1.0mm and 1.0 degree except for translation parameter perpendicular to X-ray fluoroscopic images. In the clinical applications, seven patients with the mobile-bearing TKA in deep knee bending motion were studied and analyzed. Consequently, the present technique enables us to better understand mobile-bearing TKA kinematics, and this type of evaluation was thought to be helpful for improving implant design and optimizing TKA surgical techniques.

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To suppress blurring due to cardiac movement, MRI data for the coronary arteries are generally acquired during the diastole rest period. In this research, a physician-specific model of cardiac rest period determination is proposed based on fuzzy classification system. The research aims to mimic and encapsulate the physician-specific decision in our model. From a test dataset, the cardiac rest period was determined based on normalized cross-correlation of consecutive frame images as well as normalized frame number as measured variables. The right coronary artery was used as a region of interest. The modeled physician's decision data determined the fuzzy set thresholds. The thresholds varied between subjects depending on the total number of frames following a linear function derived from the learning dataset. The distance difference of the results between the model and the modeled physician was analyzed, and shown to be significantly closer compared with the distance to other physicians with p<0.05. The results showed that the fuzzy classifier method can be used to model physician decisions.

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