Remote control of the artificial arm model using 3D hand tracking

Baron, Grzegorz; Czekalski, Piotr; Malicki, D.; Tokarz, Krzysztof

doi:10.1109/selm.2013.6562954

Cited by 7 publications

(3 citation statements)

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“…Thus, the CogInfoCom-aided manufacturing based on the WSAN control interface for robots would provide more freedom of mobility and orientation for the user compared with computer vision gesture control solutions, e.g., [24]. In such a case, the developed solution might be combined with the solution suggested in [25].…”

Section: Discussionmentioning

confidence: 99%

Wireless sensor glove interface and its application in digital holography

Mikhaylov

Pitkäaho

Tervonen

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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In the paper we introduce the novel intuitive user interface (UI) which is based on Wireless Sensor and Actuator Network (WSAN) technology and is implemented as a glove with a set of sensors for capturing user's gestures. The proposed UI is applied for interacting with a conventional 3D display and controlling the reconstruction of digital holograms. To the best of our knowledge the suggested interface is the first one, which is intended specifically for controlling the reconstruction parameters of digital holographic data. The suggested system includes the inter-cognitive communication as an important design parameter. In the paper we discuss in details the implementation of the interface and present the results of the experiments, in which the suggested UI was used to control the reconstruction of the holograms depicting industrially manufactured 3D objects. Based on the presented results, we distinguish the features of the developed UI and discuss the possible industrial applications of digital holography and of the suggested interface.

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Section: Discussionmentioning

confidence: 99%

Wireless sensor glove interface and its application in digital holography

Mikhaylov

Pitkäaho

Tervonen

et al. 2013

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom)

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“…A device often used for this purpose is Microsoft Kinect, due to the cost and complexity of a standard motion capture setup. For example, Baron et al [6] controlled a Mindstorm NXT artificial arm with sensor Kinect, employing gesture recognition to regulate arm movement. Chang et al [8] developed a Kinectbased gesture command control method for driving a humanoid robot to learn human actions, using a Kinect sensor and three different recognition mechanisms: dynamic time wrapping (DTW), Hidden Markov model (HMM) and principal component analysis (PCA).…”

Section: Related Workmentioning

confidence: 99%

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

Augello¹,

Ciulla²,

Cuzzocrea³

et al. 2020

JVLC

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The work describes a module that has been implemented for being included in a social humanoid robot architecture, in particular a storyteller robot, named NarRob. This module gives a humanoid robot the capability of mimicking and acquiring the motion of a human user in real-time. This allows the robot to increase the population of his dataset of gestures. The module relies on a Kinect based acquisition setup. The gestures are acquired by observing the typical gesture displayed by humans. The movements are then annotated by several evaluators according to their particular meaning, and they are organized considering a specific typology in the knowledge base of the robot. The properly annotated gestures are then used to enrich the narration of the stories. During the narration, the robot semantically analyses the textual content of the story in order to detect meaningful terms in the sentences and emotions that can be expressed. This analysis drives the choice of the gesture that accompanies the sentences when the story is read.

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“…It is an affordable and low-cost device that can can be used for non invasive, marker-less tracking of body gestures. As an example, Baron et al [5] controlled a Mindstorm NXT artificial arm with sensor Kinect, employing gesture recognition to regulate arm movement. Chang et al [6] developed a Kinect-based gesture command control method for driving a humanoid robot to learn human actions, using a Kinect sensor and three different recognition mechanisms: dynamic time wrapping (DTW), hidden Markov model (HMM) and principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Towards an Intelligent System for Supporting Gesture Acquisition and Reproduction in Humanoid Robots (S)

Cuzzocrea

Pilato

2020

International Conferences on Distributed Multimedia Systems

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In this paper, an intelligent system for supporting gesture acquisition and reproduction in humanoid robots, which is based on the well-known Microsoft Kinect framework, is introduced and discussed in this paper. The idea that has inspired the paper is represented by endowing an humanoid robot with the capability to mimic the motion of a human user in real time. As a further extension, the latter amenity may serve as a basis for further gesture based human-robot interactions.

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Remote control of the artificial arm model using 3D hand tracking

Cited by 7 publications

References 0 publications

Wireless sensor glove interface and its application in digital holography

Wireless sensor glove interface and its application in digital holography

An Innovative System for Supporting Acquisition and Reproduction of Gestures in Storytelling Humanoid Robots

Towards an Intelligent System for Supporting Gesture Acquisition and Reproduction in Humanoid Robots (S)

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