We investigate how human piece-moving behavior can inform expressive robot-arm motion design in the imperfect-information game Geister. First, we recorded wrist trajectories during gameplay and segmented forward moves into six phases to analyze phase-specific timing (speed and stop duration) by piece intent. We then implemented a baseline and six phase-modified robot motions on a 6-DoF arm and evaluated observers’ impressions using Godspeed Questionnaire subscales and four task-specific items. Results show that early-phase variations had limited effects, whereas late-phase timing variations—particularly faster withdrawal from a piece—significantly increased ratings of anthropomorphism, animacy, and perceived intent. These findings suggest late-phase timing variations as a practical and effective parameter for conveying internal states through robot-arm motion.

A method is presented for enhancing the steering maneuvers of human drivers using peripheral visual information generated based on the vehicle’s lateral positions on the road. Human drivers control vehicles mainly using visual feedback information received from the road. However, such feedback may sometimes become degraded due to various reasons. On the other hand, recent advances in sensory technologies make it possible to detect vehicle behavior on the road quite stably. In this paper, we investigate a way to compensate such impaired feedback using artificial visual stimuli. A series of LED devices were installed in the car’s doors to display the stimuli. We designed three types of illumination patterns (flow, position, and width conditions) that react to the current lateral position of the vehicle. Simulated driving studies were conducted to evaluate the effect of each pattern on the driver’s control of the vehicle. As a result, we found that the presenting the artificial visual stimuli without explanation could significantly reduce inappropriate steering maneuvers in the degraded feedback situations.

Human behavior involves internal states such as intentions and concentration, which are crucial in conversations and educational settings. However, these states are not always explicitly expressed. This study examines scenarios where a person pretends to be attentive but is focused on another task. We collected data from presentation scenes and used evaluators to label concentration levels through pairwise comparisons. A deep learning model was then trained to estimate these levels, showing that concentration can be accurately estimated even when individuals pretend to be focused.

In this paper, we propose a method to improve the performance of deep gaze estimation using face-shape parameters adapted to a specific target person based on multiple observations. Our gaze estimation network contains a predefined computation module that calculates gaze directions using known geometric relationships among head poses, eye-ball positions, and gaze directions. Updated face-shape parameters contribute to improving the performance of the process. In addition, the computation module enables a network to acquire the ability to induce hidden parameters such as eyeball position and eyeball radius from observed information through a training process. Experimental results reveal improvement in gaze estimation accuracy by introducing a sequential update process for face-shape parameters and a predefined computation module.

This paper proposes indirect gaze estimation from body movements as an alternative to directly observing the eye regions, as done by conventional eye trackers. This is based on the fact that changes in gaze cause corresponding changes in the movements of other body parts. Experimental results show that the proposed method can limit estimation errors to about 10 degrees.

For achieving effective human-robot interaction, robot behaviors must be designed that humans can easily predict and understand. Humanoid robots are equipped with such mechanisms as facial expressions or gestures for communicating their internal states. Although humans can easily grasp such facial expressions or gestures, unfortunately, non-humanoid robots lack such mechanisms. In this research, we employ robot behaviors and their interactions with the other robots for expressing intentions and emotions considering situations where multiple non-humanoid robots collaboratively perform tasks. Here, we focus on such interaction behaviors among robots as collision avoidance and evaluate the effects of the differences of such behaviors on human impressions of the robots. Our experimental results of the subjective evaluations showed that the human impressions of the robots were affected by not only their speed but also by their interactions, including the collision avoidance behaviors of the robots.

In this paper, we discuss an emotional touch model of a stuffed toy robot based on the user’s perception. To enable the robot’s physical contact with the user toward future bidirectional touch interaction, we investigate a comprehensive model of active touch by a stuffed toy robot on the user’s body focusing on potentially cyclic parameters of the touch, which are 1) cycle duration and 2) dragging length, to include various touches such as patting, stroking, and hugging. The testbed robot contained three servomotors to move its left arm to touch the user and make physical contact. From the result of subjective evaluation varying the two touching parameters, 1) the factor analysis extracted not only emotional parameters, “activeness,” “mildness,” and “happiness,” but also a faithful attitude, “honesty,” and 2) the contribution rate and the ANOVA for the standard factor scores showed the importance of “activeness” and “mildness,” especially affected by the cycle duration.

This research aims to develop a method to assist iterative visual search tasks, and it focuses on visual cognition to achieve effective assistance. As a first step to this goal, we analyzed the participants’ gaze behaviors when they visually recognized a target in a VR environment. In the experiment, the effect of visual cognition difficulty (VCD) is considered. Analysis results show that the participants could visually recognize lower-VCD targets at an earlier timing. This suggests that VCD-based guidance may improve task performance.

In this paper, we propose a VR-based experimental environment for analyzing the relationship among gaze and body movements using a head-mounted camera (HMD) and laser range finders (LRF). So far, we have analyzed the relations among gaze and whole body movements using a treadmill-based environment. Since the participants walked on treadmills during experiments, walking on a treadmill may be different from natural walking. Thus, in this research, we create a VR-based environment combined with LRF for tracking participants's positions. We first estimate human positions by using LRF, then generate a virtual environment corresponding to estimated human positions. The generated environment with the gaze targets is presented via HMD. We conducted a pilot experiment in this environment and show the experimental results.

In this research, we considered a scheme for recording, browsing, and sharing personal voice memos using three-dimensional (3D) auditory space, and we proposed a voice memo system that can achieve auditory localization of multiple voice memos. In the system, the users can record the voice memos and place them on the surrounding relative directions. The direction of each memo is determined by head direction during the recording. After the recording, the users can browse numerous voice memos using head directions and can define the sharing attribute of each voice memo in the edit mode so that personal and public voice memos are appropriately shared with the permitted users. In this paper, we evaluate the effectiveness of the proposed scheme when the participants conduct a series flow, from recording the voice memos to browsing the recorded voice memos, especially focusing on the effects of the user's relative. directions and head motions.

In this paper, we examine a method for stabilizing walking by using a wearable tactile expression mechanism based on a pneumatic actuator array. So far, we have researched wearable stuffed toy robots and their tactile expression mechanism for supporting going out of elderly people, especially from a cognitive perspective. In this paper, we consider a walking stabilization method using the proposed tactile expression mechanism. In this research, we focus on a "light touch" method; we aim to stabilize the wearer's walking by virtually generating a light touch using the tactile expression mechanism.

In this study, we investigate a stuffed-toy robot capable of estimating a user's emotional state via physical contact; the research is oriented toward the development of bidirectional human-robot communications. Textile-type pressure-map sensors are embedded in the robot's head and back, to facilitate detection of the location and strength of various touches via time-sequence data. Based on the recognition of such touching patterns as stroking, patting, and hugging, we conducted a verification of the estimation of the user's emotions using extracted features, such as strength and duration. The results indicate that emotional arousal was reflected by the number and strength of the touches, while the emotional valence emerged through the factor related to the context of the interaction. Our study thereby represents a possibility of understanding the user via physical contact.

Marine and lake monitoring have been received a lot of attention. We are developing a sensing device that can keep a fixed position on water autonomously for long term environmental measurement. The concepts of the device is low power consumption, low cost, omni-directional move and portable design. In this paper, we present a long-term surface monitoring system and a new prototype design of the sensing device for the monitoring system. The proposed device is aiming to move to arbitrary directions and keep its positions autonomously on ocean or lake. In the several modular experiments, we confirmed the availability and some problems of the device.

This paper proposes a depth measurement error model for consumer depth cameras such as the Microsoft Kinect, and a corresponding calibration method. These devices were originally designed as video game interfaces, and their output depth maps usually lack sufficient accuracy for 3D measurement. Models have been proposed to reduce these depth errors, but they only consider camera-related causes. Since the depth sensors are based on projector-camera systems, we should also consider projector-related causes. Also, previous models require disparity observations, which are usually not output by such sensors, so cannot be employed in practice. We give an alternative error model for projector-camera based consumer depth cameras, based on their depth measurement algorithm, and intrinsic parameters of the camera and the projector; it does not need disparity values. We also give a corresponding new parameter estimation method which simply needs observation of a planar board. Our calibrated error model allows use of a consumer depth sensor as a 3D measuring device. Experimental results show the validity and effectiveness of the error model and calibration procedure.

In this paper, we introduce a wearable message robot that transmits messages to the user and alerts him/her immediately and affectionately through physical contact. This robot is aimed at supporting elderly people who would like to participate in outings but suffer from anxiety. The system creates haptic stimuli corresponding to the user's clothing and posture. We implemented two versions of the system: the first combines haptic stimuli and anthropomorphic motion to express physical contact, while the second is a simplified system for application on smartphones to provide ubiquitous services.

In this paper, we propose an AR environment in which multiple users can synchronously share and see AR contents based on our proposed animation markers with time-sequence information. Many AR systems have been proposed, some of which consider simultaneous use by multiple users. However, these systems require a synchronization mechanism among users (devices) for simultaneous displaying. Such mechanisms complicate the systems. Thus, we propose an animation marker that can transmit temporal (frame) information in a synchronized AR environment among multiple users using our animation markers.

In this paper, we propose a portable robot to express physical contacts that are parallel to other modalities. It enfolds the user's arm in its arms and tapping the user's arm. The physical contact expressions are generated through a combination of several haptic stimuli and the robot's anthropomorphic behaviors based on its internal state. The aim of our research is building a caregiver-like robot medium. The system was designed for gentle and delicate communication between the user and the robot during a user's outings. The haptic stimuli express warm/cold, patting, and squeezing. Experimental results show that haptic communicative behaviors of the robot increase the intelligibility to the robot's messages and familiar impressions to the robot.

In this paper, we propose and evaluate a video communication system that compensates for user's uncongenial attitudes by coordinating the robot's behaviors and media control of the video. The system facilitates comfortable video communications between elderly or disabled people by an assistant robot for each user that expresses (a) active listening behaviors to compensate for the listener's attitude when he/she is not really listening to another user's talking and (b) a cover-up behavior (gaze turned to the user) to divert attention from the other user's uncongenial attitude when that person is not looking at the talking user but toward the robot at her/his side; this behavior is performed by coordinating the automatic switching of cameras to give the impression that the congenial person is still looking at the user. The results obtained in the system evaluation show the significant effectiveness of this design approach using the robot's behavior and media control of the video to compensate for the problems in video communication that we aimed to overcome.

We propose a real-time gaze estimation method based on facial-feature tracking using a single video camera that does not require any special user action for calibration. Many gaze estimation methods have been already proposed; however, most conventional gaze tracking algorithms can only be applied to experimental environments due to their complex calibration procedures and lacking of usability. In this paper, we propose a gaze estimation method that can apply to daily-life situations. Gaze directions are determined as 3D vectors connecting both the eyeball and the iris centers. Since the eyeball center and radius cannot be directly observed from images, the geometrical relationship between the eyeball centers and the facial features and eyeball radius (face/eye model) are calculated in advance. Then, the 2D positions of the eyeball centers can be determined by tracking the facial features. While conventional methods require instructing users to perform such special actions as looking at several reference points in the calibration process, the proposed method does not require such special calibration action of users and can be realized by combining 3D eye-model-based gaze estimation and circle-based algorithms for eye-model calibration. Experimental results show that the gaze estimation accuracy of the proposed method is 5° horizontally and 7° vertically. With our proposed method, various application such as gaze-communication robots, gaze-based interactive signboards, etc. that require gaze information in daily-life situations are possible.

In this paper, we propose a guide system for daily life in semipublic spaces by adopting a gaze-communicative stuffed-toy robot and a gaze-interactive display board. The system provides naturally anthropomorphic guidance through a) gaze-communicative behaviors of the stuffed-toy robot (ldquojoint attentionrdquo and ldquoeye-contact reactionsrdquo) that virtually express its internal mind, b) voice guidance, and c) projection on the board corresponding to the userpsilas gaze orientation. The userpsilas gaze is estimated by our remote gaze-tracking method. The results from both subjective/objective evaluations and demonstration experiments in a semipublic space show i) the holistic operation of the system and ii) the inherent effectiveness of the gaze-communicative guide.

This paper proposes a gaze-communicative stuffed-toy robot system with joint attention and eye-contact reactions based on ambient gaze-tracking. For free and natural interaction, we adopted our remote gaze-tracking method. Corresponding to the user's gaze, the gaze-reactive stuffed-toy robot is designed to gradually establish 1) joint attention using the direction of the robot's head and 2) eye-contact reactions from several sets of motion. From both subjective evaluations and observations of the user's gaze in the demonstration experiments, we found that i) joint attention draws the user's interest along with the user-guessed interest of the robot, ii) "eye contact" brings the user a favorable feeling for the robot, and iii) this feeling is enhanced when "eye contact" is used in combination with "joint attention." These results support the approach of our embodied gaze-communication model.

In this paper, we describe our methods of detecting human behavior in order to monitor and assist daily human tasks. To assist people in performing activities in daily life, we must be able to understand the system user's situation/state in the current task: what information is useful for the user now? Our posture-detection system using IR cameras and invisible IR pattern projectors detects the user's state as his/her 3D appearance (posture). In the proposed system, human behavior is modeled as a distribution of 3D appearances using kernel density functions, and the results of this behavior detection are used to determine the instructions to be given to the user. We show a sample implementation of the system for a toilet task with voice- and CG-animation-based instructions to users. By using this method, we can achieve effective task assistance and reveal limited visual representation of users, taking into account the human need for personal privacy. Finally, we show experimental results of using the proposed method for human tracking and behavior monitoring