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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. Source

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. Source

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. Source

Kamoda H.,Advanced Telecommunication Research Institute International ATR | Kamoda H.,Japan Broadcasting Corporation | Kitazawa S.,Wave Engineering Laboratories | Kukutsu N.,Wave Engineering Laboratories | And 3 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. Source

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. Source

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