Alle Publikationen
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2019
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(2019): Culture as a Sensor? A Novel Perspective on Human Activity Recognition. In: International Journal of Social Robotics 11 (5), S. 797-814. DOI: 10.1007/s12369-019-00590-3
DOI: https://doi.org/10.1007/s12369-019-00590-3 Abstract: Human Activity Recognition (HAR) systems are devoted to identifying, amidst the sensory stream provided by one or more sensors located so that they can monitor the actions of a person, portions related to the execution of a number of a-priori defined activities of interest. Improving the performance of systems for Human Activity Recognition is a long-standing research goal: solutions include more accurate sensors, more sophisticated algorithms for the extraction and analysis of relevant information from the sensory data, and the enhancement of the sensory analysis with general or person-specific knowledge about the execution of the activities of interest. Following the latter trend, in this article we propose the association and enhancement of the sensory data analysis with cultural information, that can be seen as an estimate of person-specific information, relieved of the burden of a long/complex setup phase. We propose a culture-aware Human Activity Recognition system which associates the recognition response provided by a state-of-the-art, culture-unaware HAR system with culture-specific information about where and when activities are most likely performed in different cultures, encoded in an ontology. The merging of the cultural information with the culture-unaware responses is done by a Bayesian Network, whose probabilistic approach allows for avoiding stereotypical representations. Experiments performed offline and online, using images acquired by a mobile robot in an apartment, show that the culture-aware HAR system consistently outperforms the culture-unaware HAR system.
2015
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(2015) : Social context perception for mobile robots In: Burgard, Wolfram: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Sept. 28, 2015 - Oct. 2, 2015, Hamburg, Germany: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Hamburg, Germany: 9/28/2015 - 10/2/2015. IEEE/RSJ International Conference on Intelligent Robots and Systems; Iros: Piscataway, NJ: IEEE, S. 3621-3627
Abstract: As robots enter human spaces, unique perception challenges are emerging. Sensing human activity, adapting to highly dynamic environments, and acting coherently and contingently is challenging when robots transition from structured environments to human-centric ones. We approach this problem by employing context-based perception, a biologically-inspired, low-cost approach to sensing that leverages noisy, global features. Across several months, our mobile robot collected real-world, multimodal data from multi-use locations; where the same space might be used for many different activities. We then ran a series of unimodal and multimodal classification experiments. We successfully classified several aspects of situational context from noisy data, and, to our knowledge are the first group to do so. This work represents an important step toward enabling robots that can readily leverage context to solve perceptual tasks.
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