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Kamoda H.,Advanced Telecommunication Research Institute International ATR | Kamoda H.,Japan Broadcasting Corporation | Kitazawa S.,Wave Engineering Laboratories | Kukutsu N.,Wave Engineering Laboratories | And 2 more authors.
IEEE Transactions on Antennas and Propagation | Year: 2015

This paper studies loop antennas over artificial magnetic conductor (AMC) surfaces with the objective of designing a dual-band RF energy harvesting antenna. The AMC surface is well known to achieve low-profile and higher gain wire antennas. From a practical point of view, impedance matching is of paramount importance to achieve highly efficient reception of weak ambient RF energy. First, the driving-point impedance of a loop antenna over an AMC surface was studied, where a conventional method using image theory to estimate the impedance was found to be not always useful for loop antennas. As the AMC surface is within the reactive near field, mutual coupling between the antenna and the AMC unit cells is significant, which the conventional method does not take into account. Then, we proposed a novel use of a polarization-dependent AMC surface for dualband RF energy harvesting. An AMC surface with a rectangular unit cell was adopted for two orthogonal polarizations with different frequencies. Finally, the AMC surface and the loop antennas were successfully implemented as a dual-band energy harvesting panel together with RF-to-dc conversion circuits and a power management circuit. © 2015 IEEE.

Ogawa T.,Advanced Telecommunication Research Institute International ATR | Hirayama J.-I.,Advanced Telecommunication Research Institute International ATR | Gupta P.,Advanced Telecommunication Research Institute International ATR | Moriya H.,Advanced Telecommunication Research Institute International ATR | And 6 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2015

Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies. © 2015 IEEE.

Kauppi J.-P.,University of Helsinki | Kauppi J.-P.,Aalto University | Hahne J.,TU Berlin | Hahne J.,Universitatsmedizin Gottingen | And 4 more authors.
PLoS ONE | Year: 2015

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. © 2015 Kauppi et al.

Alimardani M.,Osaka University | Alimardani M.,Advanced Telecommunication Research Institute International ATR | Shuichi N.,Advanced Telecommunication Research Institute International ATR | Ishiguro H.,Osaka University | Ishiguro H.,Advanced Telecommunication Research Institute International ATR
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics | Year: 2014

Users of a brain-computer interface (BCI) learn to co-adapt with the system through the feedback they receive. Particularly in case of motor imagery BCIs, feedback design can play an important role in the course of motor imagery training. In this paper we investigated the effect of biased visual feedback on performance and motor imagery skills of users during BCI control of a pair of humanlike robotic hands. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback. We discuss how this effect could be possibly due to the humanlike design of feedback and occurrence of body ownership illusion. Our findings suggest that in general training protocols for BCIs, realistic feedback design and subject's self-evaluation of performance can play an important role in the optimization of motor imagery skills.

Wang W.,TU Munich | Brscic D.,Advanced Telecommunication Research Institute International ATR | He Z.,TU Munich | Hirche S.,TU Munich | Kuhnlenz K.,TU Munich
URAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence | Year: 2011

Real time human body motion estimation plays an important role in the perception for robotics nowadays, especially for the applications of human robot interaction and service robotics. In this paper, we propose a method for real-time 3D human body motion estimation based on 3-layer laser scans. All the useful scanned points, presenting the human body contour information, are subtracted from the learned background of the environment. For human contour feature extraction, in order to avoid the situations of unsuccessful segmentation, we propose a novel iterative template matching algorithm for clustering, where the templates of torso and hip sections are modeled with different radii. Robust distinct human motion features are extracted using maximum likelihood estimation and nearest neighbor clustering method. Subsequently, the positions of human joints in 3D space are retrieved by associating the extracted features with a pre-defined articulated model of human body. Finally we demonstrate our proposed methods through experiments, which show accurate human body motion tracking in real time. © 2011 IEEE.

Brscic D.,Advanced Telecommunication Research Institute International ATR
2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014 | Year: 2014

Robotic technology is gradually entering our everyday environments and it is expected that in future social robots will be used to provide various services to users of public spaces. However, robots nowadays still lack the abilities to sense and understand their surroundings, and even more importantly, they lack the knowledge on the persons' usual behavior in public spaces. This talk will discuss how we use ambient sensors to overcome the difficulties of introducing robots in populated spaces and to improve our understanding on the persons' behavior, as well as give an overview of our group's research on human-robot interaction in public environments. © 2014 IEEE.

Kuwamura K.,Advanced Telecommunication Research Institute International ATR | Yamazaki R.,Advanced Telecommunication Research Institute International ATR | Nishio S.,Advanced Telecommunication Research Institute International ATR | Ishiguro H.,Advanced Telecommunication Research Institute International ATR
Gerontechnology | Year: 2014

Purpose: Having a limited social network increases the risk of dementia1. Conversation is important to avoid or calm dementia and decreasing anxiety to suppress Behavioral and Psychological Symptoms of Dementia. (BPSD). However, most care facilities suffer from a shortage of workers resulting in a lack of communication with residents. To solve this, we have developed a telecommunication robot "Telenoid" that encourages residents to communicate. Method: Telenoid is a teleoperated android with a humanlike design that can represent any person (Figure 1). It is about 50cm long, weights 3.2kg and is covered with a material that resembles soft skin. Through the Internet, it can be teleoperated from anywhere in the world with a laptop or PC and a headset. Telenoid has nine independent actuators to synchronize itself with teleoperator's motion; it can speak, look around, and give a hug with its arms. We have run various field experiments using Telenoid with the elderly, especially those with dementia, to encourage conversation with others and investigate its effect on BPSD. Results & Discussion: Telenoid's appearance may provide a negative impression. However, once people hug and interact with it, the impression becomes positive2 (Figure 2). This effect is much stronger for elderly individuals, who are attracted to Telenoid from the beginning (Figure 3). Through field studies in Japan and Denmark, we found strong effects to the elderly with dementia. Telenoid induced active communication to the elderly with mild dementia, and physical interaction to those with severe dementia3,4. Several other research projects have been explored, such as philosophical studies on humanness, investigating cognitive aspects of dementia, and Danish national project to shorten patients' duration in hospitals (Patient@ Home). When introducing new equipment such as Telenoid to care facilities, we cannot just pass them; we need to carefully consider various topics such as training course for staff to understanding its usage and effects, and guidelines for proper utility. We will report on such and result of long-term trial in care facilities as well.

Yamazaki R.,Advanced Telecommunication Research Institute International ATR | Kuwamura K.,Advanced Telecommunication Research Institute International ATR | Nishio S.,Advanced Telecommunication Research Institute International ATR | Minato T.,Advanced Telecommunication Research Institute International ATR | Ishiguro H.,Advanced Telecommunication Research Institute International ATR
Gerontechnology | Year: 2014

Purpose: As the aged population grows, social isolation among senior citizens is one of the leading issues in healthcare promotion. Depression and dementia are the most common forms of mental illness among seniors, and related to functional decline; depression may even increase the risk of incident dementia1. Social isolation is a huge risk factor for the onset of depression, and those without close social ties have an increased risk for developing dementia2. To solve the isolation issue, and improve seniors' well-being by enhancing social connectedness, we propose to employ a teleoperated android robot named Telenoid. By focusing on dementia care, we aim to evaluate the effect of the android on the older person's wellbeing. Method: Telenoid is designed to represent a human presence that can be perceived as anybody. The objective of this minimal design feature is to instill the feeling that a distant interlocutor is actually close to the user. We introduced Telenoid into a care facility ten times intermittently during two months to observe older subjects' changes in attitude over time (Figure 1). As a pilot study, mainly focusing on only two female cases of dementia, we conducted a quantitative and qualitative study that collected narrative and behavioral data from the subjects during conversations. When interacting, the android/teleoperator spoke to them and replied with nodding and hugging them. The residents' behaviors were observed via video recordings, which were coded and analysed. Results & Discussion: The study shows that the elderly residents developed prosocial behaviors and increasingly positive attitudes toward Telenoid. One resident, who was aggressive due to dementia, started to calm down and gradually increased her interaction, verbally and non-verbally, with Telenoid while showing prosocial behaviors such as stroking its head and attempting to give it food (Figure 2). Also, another resident who tended to be isolated and stayed in her room showed a strong attachment to Telenoid from the beginning, started to come out to see it and expanded the various ways of interaction, e.g., by sharing the conversation with other residents. Telenoid encouraged the elderly subjects to be more communicative over time. The prospect of verifying the android's effects on senior citizens in further longitudinal studies is promising. Including possible, beneficial secondary effects on the operators of Telenoid, we consider the conditions for creating a remote community that can promote seniors' integration.

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