2013 International Symposium on Electrodynamic and Mechatronic Systems (SELM) 2013
DOI: 10.1109/selm.2013.6562954
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Remote control of the artificial arm model using 3D hand tracking

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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%
“…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%
“…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%
“…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%