Japanese society is ageing ever faster. One of the most critical issues here is the shortage of working population, which is both cause and effect of the `super-ageing' problem. We propose a new framework to `desterilize' and utilize the elderly population as a new social resource. To sustain and hopefully enhance cognitive and physical functions of the elderly, we integrate cognitive and physical interventions by using high-fidelity (Hi-Fi) virtual/augmented reality (Lucid Virtual/Augmented Reality, LVAR) and an endoskeletal robot suit (StillSuit), respectively. LVAR has a physics-fidelity (Phy-Fi) digital-self for each LVAR space, and provides real time dynamic feedbacks to the user through StillSuit. Physical interventions are governed with a biologically relevant musculoskeletal model tailored for each user. To realize social cognition, furthermore, LVAR provides a social networking service with high quality 3D immersive experiences, which is able to be shared by remote users. With the fine-tuned interventions based on biological data of human and non-human animals, we prolong the healthy life expectancy of the elderly population to be the social resource, by which we will overcome the negative spiral of the super-ageing society.

We propose a method of extracting watermark from 3D prints created from 3D mesh data. The watermark is embedded to the 3D mesh in the spectral domain using a robust, imperceptible, and informed algorithm based on the spread spectrum technique, and then extracted from 3D prints by reconstructing the 3D mesh homologous to the original. A suspect 3D mesh is reconstructed accurately and robustly from noisy and incomplete 3D scans of the 3D prints, by optimizing a sparse subset of the spectrum using a variant of the iterative closest point technique. The mesh is registered to the original mesh by construction, in terms of geometry and topology, which allows us to extract a suspect watermark by simple algebraic operations. Comprehensive experiments have been conducted to demonstrate the performance of our method using standard 3D datasets and multiple 3D printers. The proposed method significantly outperforms prior methods under different conditions in the practical scenario. The probability of false positive detection is kept less than 106 for the most cases of simulated and real data.

We present a method of modeling the three-dimensional shape of a deforming object from a time-series of range images. A non-rigid variant of ICP algorithm is proposed to solve the correspondence between the range images. The deformation is modeled by the combination of piecewise similarity transformation using differential coordinates, and solved stably and efficiently by alternating minimization of a quadratic objective function. The performance of the proposed method is demonstrated by conducting two modeling tasks. First, the complete shapes of human hand and full-body are reconstructed by integrating the range images acquired by a single depth camera. Second, the shape completion of four-dimensional scan data is carried out by fitting a template model to the point clouds by extending our registration technique in the spatiotemporal domain.

We propose a novel color code, Hamming color code, designed for rapid 3D shape acquisition using structured light projection. The Hamming color code has several properties which are desirable for practical 3D acquisition as follows. First, the Hamming distance of adjacent colors is always 1, which makes the color detection robust to color blending due to defocusing, subsurface scattering, or chromatic aberration. Second, the substrings of a certain length is guaranteed to be unique. In other words, the Hamming code can be viewed as a subset of de Bruijn sequence. Third, a one-dimensional coordinate can be encoded for each pixel, which enables dense 3D reconstruction from a single pattern projection. Thanks to the uniqueness and robustness of the substrings, the structured light can be decoded stably by dynamic programming. We have implemented parallel dynamic programming on GPU and achieved the speed-up by a factor of 630 compared to the CPU-based implementation, and accomplished video-rate 3D acquisition using commodity hardware. Several experiments have been conducted to demonstrate the stability and performance of our algorithm. Finally we discuss the limitation and future direction of this work.

We present an actuated handheld puppet system for controlling the posture of a virtual character. Physical puppet devices have been used in the past to intuitively control character posture. In our research, an actuator is added to each joint of such an input device to provide physical feedback to the user. This enhancement offers many benefits. First, the user can upload pre-defined postures to the device to save time. Second, the system is capable of dynamically adjusting joint stiffness to counteract gravity, while allowing control to be maintained with relatively little force. Third, the system supports natural human body behaviors, such as whole-body reaching and joint coupling. This paper describes the user interface and implementation of the proposed technique and reports the results of expert evaluation. We also conducted two user studies to evaluate the effectiveness of our method.

By understanding the behavior, satisfaction level, or values of the customer, the productivity and level of customer satisfaction of a service industry can be improved. Such customer-based considerations are estimated from questionnaire data in a general manner. The useful estimation of such considerations requires effective methods for modeling the cognitive structures of customers based on such data. However, it is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. The present paper describes a method of constructing a probabilistic model of the cognitive structure of the customer, which clarifies the satisfaction level and decision making process of the customer of a retail service through statistical graphical modeling. The proposed method constructs a probabilistic cognitive structure model by integrating questionnaire data and a Bayesian network, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The model structure can be constructed automatically based on information criteria and can embed some of the experiences of the model designer and/or physical or social rules in advance. The proposed method is applied to an analysis of the requested function from customers regarding the continued use of an item of interest. We obtained useful knowledge for function design and marketing from the model constructed by a simulation and sensitivity analysis. The proposed method can be applied to various services that use a variety of data.

In this paper, an easy calibration method for projector is proposed. The calibration handled in this paper is projective relation between 3D space and 2D pattern, and is not correction of trapezoid distortion in projected pattern. In projector-camera systems, especially for 3D measurement, such calibration is the basis of process. The projection from projector can be modeled as inverse projection of the pinhole camera, which is generally considered as perspective projection. In the existing systems, some special objects or devices are often used to calibrate projector, so that 3D-2D projection map can be measured for typical camera calibration methods. The proposed method utilizes projective geometry between camera and projector, so that it requires only pre-calibrated camera and a plane. It is easy to practice, easy to calculate, and reasonably accurate.

Accurate and quantitative analysis of human hand motion requires an understanding of the kinematics of the proximal and distal interphalangeal joints in three dimensions. This paper presents a technique to investigate in-vivo finger joint kinematics using magnetic resonance (MR) imaging. Bone surface models were extracted from MR images of the right hand of ten healthy subjects in four different postures. The relative motion of adjacent bones for all subjects was calculated and expressed as the helical axis and the angles around the axes of local bone coordinate system. The motion data indicated the change of axis orientation with flexion. After carefully selecting the subject and a pose with an appropriate flexional angle, studies determined that the mean inclination between the axes of the proximal and distal interphalangeal joints was approximately 14 [deg].

This paper proposes the total protocol to determine the link structure of a human hand using optical motion capture data. The difficulty in capturing hand motion comes from the hand's relatively high degrees of freedom located in very small space. To deal with the problem caused by closeness between markers, the number of markers is reduced. The effect of reduced markers is compensated by simplifying the problem. The adverse effect of skin movement and the limited range of movement is avoided by regulating the calibration motion appropriately. A link estimation experiment is performed to show the effectiveness of the proposed method.

This paper deals with the modeling of the human hand posture of object operation. Preliminary observation experiment has been conducted on mouse operation by 5 subjects. The contact area of both on hands and on mice shows us that there are some patterns in the strategy of how to locate the basis of the hand with respect to the mouse. In addition, the preference of joint angle acuteness has been observed. The hand model and the method to acquire and organize the quantitative data are shown.