2018 |
A Zlatintsi, I Rodomagoulakis, P Koutras, A ~C Dometios, V Pitsikalis, C ~S Tzafestas, P Maragos Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot Conference Proc. IEEE Int'l Conf. Acous., Speech, and Signal Processing, Calgary, Canada, 2018. Abstract | BibTeX | Links: [PDF] @conference{ZRK+18, title = {Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot}, author = {A Zlatintsi and I Rodomagoulakis and P Koutras and A ~C Dometios and V Pitsikalis and C ~S Tzafestas and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/Zlatintsi+_I-SUPPORT_ICASSP18.pdf}, year = {2018}, date = {2018-04-01}, booktitle = {Proc. IEEE Int'l Conf. Acous., Speech, and Signal Processing}, address = {Calgary, Canada}, abstract = {We explore new aspects of assistive living on smart human-robot interaction (HRI) that involve automatic recognition and online validation of speech and gestures in a natural interface, providing social features for HRI. We introduce a whole framework and resources of a real-life scenario for elderly subjects supported by an assistive bathing robot, addressing health and hygiene care issues. We contribute a new dataset and a suite of tools used for data acquisition and a state-of-the-art pipeline for multimodal learning within the framework of the I-Support bathing robot, with emphasis on audio and RGB-D visual streams. We consider privacy issues by evaluating the depth visual stream along with the RGB, using Kinect sensors. The audio-gestural recognition task on this new dataset yields up to 84.5%, while the online validation of the I-Support system on elderly users accomplishes up to 84% when the two modalities are fused together. The results are promising enough to support further research in the area of multimodal recognition for assistive social HRI, considering the difficulties of the specific task.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We explore new aspects of assistive living on smart human-robot interaction (HRI) that involve automatic recognition and online validation of speech and gestures in a natural interface, providing social features for HRI. We introduce a whole framework and resources of a real-life scenario for elderly subjects supported by an assistive bathing robot, addressing health and hygiene care issues. We contribute a new dataset and a suite of tools used for data acquisition and a state-of-the-art pipeline for multimodal learning within the framework of the I-Support bathing robot, with emphasis on audio and RGB-D visual streams. We consider privacy issues by evaluating the depth visual stream along with the RGB, using Kinect sensors. The audio-gestural recognition task on this new dataset yields up to 84.5%, while the online validation of the I-Support system on elderly users accomplishes up to 84% when the two modalities are fused together. The results are promising enough to support further research in the area of multimodal recognition for assistive social HRI, considering the difficulties of the specific task. |
2016 |
I. Rodomagoulakis, N. Kardaris, V. Pitsikalis, A. Arvanitakis, P. Maragos A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results Conference Proceedings - International Conference on Image Processing, ICIP, 2016-August , 2016, ISSN: 15224880. Abstract | BibTeX | Links: [PDF] @conference{334, title = {A multimedia gesture dataset for human robot communication: Acquisition, tools and recognition results}, author = { I. Rodomagoulakis and N. Kardaris and V. Pitsikalis and A. Arvanitakis and P. Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/RKPAM_MultimedaGestureDataset-HRI_ICIP2016.pdf}, doi = {10.1109/ICIP.2016.7532923}, issn = {15224880}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings - International Conference on Image Processing, ICIP}, volume = {2016-August}, pages = {3066--3070}, abstract = {Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Motivated by the recent advances in human-robot interaction we present a new dataset, a suite of tools to handle it and state-of-the-art work on visual gestures and audio commands recognition. The dataset has been collected with an integrated annotation and acquisition web-interface that facilitates on-the-way temporal ground-truths for fast acquisition. The dataset includes gesture instances in which the subjects are not in strict setup positions, and contains multiple scenarios, not restricted to a single static configuration. We accompany it by a valuable suite of tools as the practical interface to acquire audio-visual data in the robotic operating system, a state-of-the-art learning pipeline to train visual gesture and audio command models, and an online gesture recognition system. Finally, we include a rich evaluation of the dataset providing rich and insightfull experimental recognition results. |
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