Gesture Recognition Supporting the Interaction of Humans with Socially Assistive Robots

Michel, Damien; Papoutsakis, Konstantinos; Argyros, Antonis A.

doi:10.1007/978-3-319-14249-4_76

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…For the segmentation a Random Forest classifier is used on a superpixel segmentation, while the gestures are classified using Exemplar SVMs. Another approach for hand gesture recognition with an head mounted camera has been presented in [25]. This approach is based on the analysis of a skeleton extracted from the hand but the experimental evaluation is limited to 3 static gestures and 5 dynamic ones.…”

Section: Related Workmentioning

confidence: 99%

Head-mounted gesture controlled interface for human-computer interaction

Memo

Zanuttigh

2016

Multimed Tools Appl

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This paper proposes a novel human-computer interaction system exploiting gesture recognition. It is based on the combined usage of an head-mounted display and a multi-modal sensor setup including also a depth camera. The depth information is used both to seamlessly include augmented reality elements into the real world and as input for a novel gesture-based interface. Reliable gesture recognition is obtained through a real-time algorithm exploiting novel feature descriptors arranged in a multi-dimensional structure fed to an SVM classifier. The system has been tested with various augmented reality applications including an innovative human-computer interaction scheme where virtual windows can be arranged into the real world observed by the user

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Section: Related Workmentioning

confidence: 99%

Head-mounted gesture controlled interface for human-computer interaction

Memo

Zanuttigh

2016

Multimed Tools Appl

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“…For detection of the user and recognition of a pointing gesture we use the work presented by Michel et al in [32] combined with a full body skeleton tracking solution. This method is based on the detection and tracking of body parts across RGB-D frames, using a layered representation of a hand model and comparing a set of possible candidates in terms of geometric shape and trajectory properties.…”

Section: A Perception Of Pointing Gesturementioning

confidence: 99%

Grasping Objects From the Floor in Assistive Robotics: Real World Implications and Lessons Learned

et al. 2019

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This paper presents a system enabling a mobile robot to autonomously pick-up objects a human is pointing at from the floor. The system does not require object models and is designed to grasp unknown objects. The robot decides by itself if an object is suitable for grasping by considering measures of size, position and the environment suitability. The implementation is built on the second prototype of the home care robot Hobbit, thereby verifying that complex robotic manipulation tasks can be performed with economical hardware. The presented system was already tested in real apartments with elderly people. We highlight this by discussing the additional complexity for complete autonomous behavior in apartments compared with tests in labs.

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“…For supporting gesture based interaction a framework has been implemented based on the approach discussed in (Michel et al, 2014). The adopted framework encompasses a collection of techniques that enable robust, real-time and efficient gesture recognition based on visual information acquired by an RGBD camera.…”

Section: Gesture-based Interactionmentioning

confidence: 99%

3D Vizualization of Large Scale Data Centres

Drossis

Birliraki

Patsiouras

et al. 2016

Proceedings of the 6th International Conference on Cloud Computing and Services Science

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This paper reports on ongoing work regarding interactive 3D visualization of large scale data centres in the context of Big Data and data centre infrastructure management. The proposed approach renders a virtual area of real data centres preserving the actual arrangement of their servers and visualizes their current state while it notifies users for potential server anomalies. The visualization includes several condition indicators, updated in real time, as well as a color-coding scheme for the current servers' condition referring to a scale from normal to critical. Furthermore, the system supports on demand exploration of an individual server providing detailed information about its condition, for a specific timespan, combining historical analysis of previous values and the prediction of potential future state. Additionally, natural interaction through hand-gestures is supported for 3D navigation and item selection, based on a computer-vision approach.

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Gesture Recognition Supporting the Interaction of Humans with Socially Assistive Robots

Cited by 11 publications

References 15 publications

Head-mounted gesture controlled interface for human-computer interaction

Head-mounted gesture controlled interface for human-computer interaction

Grasping Objects From the Floor in Assistive Robotics: Real World Implications and Lessons Learned

3D Vizualization of Large Scale Data Centres

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