Advanced Telecommunications Research Institute
Advanced Telecommunications Research Institute
Sawabe T.,Nara Institute of Science and Technology |
Kanbara M.,Nara Institute of Science and Technology |
Hagita N.,Advanced Telecommunications Research Institute
Adjunct Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2016 | Year: 2016
This paper proposes the Diminished Reality (DR) method for an acceleration stimulus to reduce motion sickness for an autonomous vehicle by presenting vection for user before the real acceleration occurs. The technology of an autonomous vehicle has been rapidly developed in all over the world. Instead of controlling vehicle by passenger themselves, the autonomous system helps them acceleration and deceleration controls and safety controls as well. However, it is predictable that the number of passengers who get motion sickness increases because they receive an unexpected acceleration stimulus for the autonomous driving.In the field of the Virtual Reality, the technology to create virtual acceleration stimulus to the passenger in the driving simulator or flight simulator have been developed. However, our approach for using pseudo to reduce the acceleration stimulus which occurs in autonomous driving to prevent the motion sickness is the opposite problem of these conventional VR researches. The real acceleration stimulus from the vehicle is reduced by presenting vection before the real acceleration occurs.In this research, we demonstrate the idea of technology that use the vection with an augmented reality system to reduce an effect of the real acceleration in the vehicle which mainly the factor for the motion sickness. © 2016 IEEE.
Ugur E.,University of Innsbruck |
Ugur E.,Advanced Telecommunications Research Institute |
Nagai Y.,Osaka University |
Celikkanat H.,Middle East Technical University |
And 2 more authors.
Robotica | Year: 2015
Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called motionese7. The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese. © 2014 Cambridge University Press.
Sakata M.,Advanced Telecommunications Research Institute |
Yucel Z.,Advanced Telecommunications Research Institute |
Shinozawa K.,Advanced Telecommunications Research Institute |
Hagita N.,Advanced Telecommunications Research Institute |
And 3 more authors.
ACM Transactions on Management Information Systems | Year: 2013
Common periodical health check-ups include several clinical test items with affordable cost. However, these standard tests do not directly indicate signs of most lifestyle diseases. In order to detect such diseases, a number of additional specific clinical tests are required, which increase the cost of the health check-up. This study aims to enrich our understanding of the common health check-ups and proposes a way to estimate the signs of several lifestyle diseases based on the standard tests in common examinations without performing any additional specific tests. In this manner, we enable a diagnostic process, where the physician may prefer to perform or avoid a costly test according to the estimation carried out through a set of common affordable tests. To that end, the relation between standard and specific test results is modeled with a multivariate kernel density estimate. The condition of the patient regarding a specific test is assessed following a Bayesian framework. Our results indicate that the proposed method achieves an overall estimation accuracy of 84%. In addition, an outstanding estimation accuracy is achieved for a subset of high-cost tests.Moreover, comparison with standard artificial intelligence methods suggests that our algorithm outperforms the conventional methods. Our contributions are as follows: (i) promotion of affordable health check-ups, (ii) high estimation accuracy in certain tests, (iii) generalization capability due to ease of implementation on different platforms and institutions, (iv) flexibility to apply to various tests and potential to improve early detection rates. © 2013 ACM.
Callan D.E.,Osaka University |
Callan D.E.,Universal Communication Research Institute |
Terzibas C.,Universal Communication Research Institute |
Cassel D.B.,Locomobi Inc |
And 2 more authors.
Frontiers in Human Neuroscience | Year: 2016
The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0-352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity related to piloting the airplane. © 2016 Callan, Terzibas, Cassel, Sato and Parasuraman.
Becker-Asano C.,Advanced Telecommunications Research Institute |
Kanda T.,Advanced Telecommunications Research Institute |
Ishi C.,Advanced Telecommunications Research Institute |
Ishiguro H.,Osaka University
AI and Society | Year: 2011
To let humanoid robots behave socially adequate in a future society, we started to explore laughter as an important para-verbal signal known to influence relationships among humans rather easily. We investigated how the naturalness of various types of laughter in combination with different humanoid robots was judged, first, within a situational context that is suitable for laughter and, second, without describing the situational context. Given the variety of human laughter, do people prefer a certain style for a robot's laughter? And if yes, how does a robot's outer appearance affect this preference, if at all? Is this preference independent of the observer's cultural background? Those participants, who took part in two separate online surveys and were told that the robots would laugh in response to a joke, preferred one type of laughter regardless of the robot type. This result is contrasted by a detailed analysis of two more surveys, which took place during presentations at a Japanese and a German high school, respectively. From the results of these two surveys, interesting intercultural differences in the perceived naturalness of our laughing humanoids can be derived and challenging questions arise that are to be addressed in future research. © 2010 Springer-Verlag London Limited.
Ugurlu B.,Ozyegin University |
Ugurlu B.,Advanced Telecommunications Research Institute |
Nishimura M.,Toyota Technological Institute |
Nishimura M.,Suzuki Motor Corporation |
And 3 more authors.
IEEE Transactions on Human-Machine Systems | Year: 2015
This paper presents a wearable upper body exoskeleton system with a model-based compensation control framework to support robot-aided shoulder-elbow rehabilitation and power assistance tasks. To eliminate the need for EMG and force sensors, we exploit off-the-shelf compensation techniques developed for robot manipulators. Thus, target rehabilitation tasks are addressed by using only encoder readings. A proof-of-concept evaluation was conducted with five able-bodied participants. The patient-active rehabilitation task was realized via observer-based user torque estimation, in which resistive forces were adjusted using virtual impedance. In the patient-passive rehabilitation task, the proposed controller enabled precise joint tracking with a maximum positioning error of 0.25°. In the power assistance task, the users' muscular activities were reduced up to 85% while exercising with a 5 kg dumbbell. Therefore, the exoskeleton system was regarded as being useful for the target tasks, indicating that it has a potential to promote robot-aided therapy protocols. © 2013 IEEE.
Yano K.,Advanced Telecommunications Research Institute |
Suyama T.,Advanced Telecommunications Research Institute
PRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging | Year: 2016
In this paper, we propose a fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. We explicitly derive the loss function from the conventional BCI approach and solve its minimization by optimization with a non-convex fixed low-rank constraint. For evaluation, we conducted an experiment to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. Our results show competitive performance against conventional methods using CSP. The advantage of the proposed method is the holistic approach, which combines feature extraction, feature selection and classification. The obtained results are also plausible from the standpoint of neurophysiological interpretation. © 2016 IEEE.