The aim of this study is to present a modulation index (MI) for volumetric modulated arc therapy (VMAT) based on the speed and acceleration analysis of modulating-parameters such as multi-leaf collimator (MLC) movements, gantry rotation and dose-rate, comprehensively. The performance of the presented MI (MIt) was evaluated with correlation analyses to the pre-treatment quality assurance (QA) results, differences in modulating-parameters between VMAT plans versus dynamic log files, and differences in dose-volumetric parameters between VMAT plans versus reconstructed plans using dynamic log files. For comparison, the same correlation analyses were performed for the previously suggested modulation complexity score (MCS(v)), leaf travel modulation complexity score (LTMCS) and MI by Li and Xing (MI Li&Xing). In the two-tailed unpaired parameter condition, p values were acquired. The Spearman's rho (r(s)) values of MIt, MCSv, LTMCS and MI Li&Xing to the local gamma passing rate with 2%/2 mm criterion were -0.658 (p < 0.001), 0.186 (p = 0.251), 0.312 (p = 0.05) and -0.455 (p = 0.003), respectively. The values of rs to the modulating-parameter (MLC positions) differences were 0.917, -0.635, -0.857 and 0.795, respectively (p < 0.001). For dose-volumetric parameters, MIt showed higher statistically significant correlations than the conventional MIs. The MIt showed good performance for the evaluation of the modulation-degree of VMAT plans.

In an environment where the contexts of users are complex and the degree of freedom of user activity is very high, such as in daily life, several factors need to be considered for constructing user models. Such a model should include changes in the meanings of activities that reflect the user's situation both temporally and individually. In this paper we propose a novel approach for personalizing the user model and adapting it to individual circumstances with a wearable sensor network. We also describe the process for determining the repetitive activities of a user by using incremental clustering and Bayesian network. We show experimental results for an adaptive user model based on a real wearable sensor platform. Multimedia data of user experience are acquired from the multimodal sensors, and processed to metadata that have meanings.

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Teleoperation provides a technical means to perform desired tasks in remote environments. Teleoperation using direct control and haptic-mediated interactions requires significant effort for unskilled users to understand how to operate the robot. Task-oriented teleoperation supports human-level understanding to directly transfer the meaning of tasks from the user to the robot. In this paper, we propose a natural 3D interface to transfer task knowledge to a remote robot. We design a 3D manipulation system in a mixed reality environment that combines human hand gestures and a 3D view of the remote robot. We demonstrate that the remote robot successfully executes the ordered task even in dynamic environments.

This paper describes an intuitive approach for a cognitive grasp of a robot. The cognitive grasp means the chain of processes that make a robot to learn and execute a grasping method for unknown objects like a human. In the learning step, a robot looks around a target object to estimate the 3D shape and understands the grasp type for the object through a human demonstration. In the execution step, the robot correlates an unknown object to one of known grasp types by comparing the shape similarity of the target object based on previously learned models. For this cognitive grasp, we mainly deal with two functionalities such as reconstructing an unknown 3D object and classifying the object by grasp types. In the experiment, we evaluate the performance of object classification according to the grasp types for 20 objects via human demonstration.