This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.

In order to perform the registration of virtual objects in vision-based augmented reality systems, the estimation of the relation between the real and virtual worlds is needed. This paper presents a three-point vision-based registration method for video see-through augmented reality systems using binocular cameras. The proposed registration method is based on a combination of monocular and stereoscopic registration methods. A correction method that performs an optimization of the registration by correcting the 2D positions in the images of the marker feature points is proposed. Also, an extraction strategy based on color information is put forward to allow the system to be robust to fast user's motion. In addition, a quantification method is used in order to evaluate the stability of the produced registration. Timing and stability results are presented. The proposed registration method is proven to be more stable than the standard stereoscopic registration method and to be independent of the distance. Even when the user moves quickly, our developed system succeeds in producing stable three-point based registration. Therefore, our proposed methods can be considered as interesting alternatives to produce the registration in binocular augmented reality systems when only three points are available.

In recent years, autonomous driving technologies are being developed for vehicles and personal mobility devices including golf carts and autonomous wheelchairs for various use cases, not only outside areas but inside areas like shopping malls, hospitals and airpots. The main purpose of developing these autonomous vehicles is to avoid the traffic accidents caused by human errors, to assist people with walking, and to improve human comfort by relieving them from driving. Most relevant research focuses on the efficiency and safety of autonomous driving, however, in order to use by the widespread of people in the society, it is important to consider passenger comfort inside vehicles as well as safety and efficiency. Therefore, in this work, we emphasize the importance of considering passenger comfort in designing the control loop of autonomous navigation for the concept of comfortable intelligence in the future autonomous mobility. Moreover, passenger characteristics, in terms of ride comfort in an autonomous vehicle, have not been investigated with regard to safety and comfort, depending on each passenger's driving experience, habits, knowledge, personality, and preference. There are still few studies on the optimization of autonomous driving control reflecting passenger characteristics and different stress factors during the ride. In this study, passenger stress characteristics with different stress factors were objectively analyzed using physiological indices (heart rate and galvanic skin response sensors) during autonomous wheelchair usages. Two different experimental results from 12 participants suggest that there are always at least two types of passengers: one who experiences stress and the other who does not, depending on the stress factors considered. Moreover, with regard to the classification result for the stress reduction method, there are two types of passenger groups, for whom the solution method is, respectively, either effective or ineffective.

Acquisition of three-dimensional information of a real-world scene from two-dimensional images has been one of the most important issues in computer vision and image understanding in the last two decades. Noncontact range acquisition techniques can be essentially classified into two classes: Passive and active. This paper concentrates on passive depth extraction techniques which have the advantage that 3-D information can be obtained without affecting the scene. Passive range sensing techniques are often referred to as shape-from-x, where x is one of visual cues such as shading, texture, contour, focus, stereo, and motion. These techniques produce 2.5-D representations of visible surfaces. This survey discusses aspects of this research field and reviews some recent advances including video-rate range imaging sensors as well as emerging themes and applications.

Interaction between emergency medical technicians (EMTs) and doctors is essential in emergency medical care. Doctors require diverse information related to a patient to provide efficient aid. In 2005, we started the Ikoma119 project and have developed a ubiquitous communication platform for emergency medical care called Mobile ER. Our platform, which is based on wireless internet technology, has such desirable properties as low-cost, location-independent service, and ease of service introduction. We provide an overview of our platform and describe the services that we have developed. We also discuss the remaining issues to realize our platform's actual operation.

This paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.