Mobile shopping cart application using kinect

Gai, Shengnan; Jung, Eui-Jung

doi:10.1109/urai.2013.6677370

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…Human Robot Interaction (HRI) has also become a hot topic and many works have been claimed at creating robotic devices that can mimic human actions. Gestures are crucial in HRI for robots to follow what humans do or respond to their gestural commands [7,27]. Apart from hand gesture recognition, palm center and finger recognition have been researched over the past several years.…”

Section: Literature Reviewmentioning

confidence: 99%

A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Sokhib

Whangbo

2019

IAJIT

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Kinect is a promising acquisition device that provides useful information on a scene through color and depth data. There has been a keen interest in utilizing Kinect in many computer vision areas such as gesture recognition. Given the advantages that Kinect provides, hand gesture recognition can be deployed efficiently with minor drawbacks. This paper proposes a simple and yet efficient way of hand gesture recognition via segmenting a hand region from both color and depth data acquired by Kinect v1. The Inception model of the image recognition system is used to check the reliability of the proposed method. Experimental results are derived from a sample dataset of Microsoft Kinect hand acquisitions. Under the appropriate conditions, it is possible to achieve high accuracy in close to real time

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Section: Literature Reviewmentioning

confidence: 99%

A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Sokhib

Whangbo

2019

IAJIT

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“…One of such a kind is described in [22]. The Mobile Shopping Cart (MSC) uses the Kinect sensor to detect customers and arm gesture recognition.…”

Section: A Elderly and Agedmentioning

confidence: 99%

Human-robot collaboration: A survey

Chandrasekaran

Conrad

2015

SoutheastCon 2015

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Human-Machine collaboration is a vastly developing field in the area of Robotics. This paper introduces the concept of such collaboration and describes its use in various facets of our society. The various kinds of interaction between humans and robots in applications such as elderly care, schools and education, medicine, military and space exploration have been reviewed in this paper. Also, the learning process used by the robot for interacting with the human and environment is presented.

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“…Another HR design research group has focused particularly on detection and prevention of falling of elderly people at home (Vincze et al, 2014). HRs have also been equipped with perception sensors to detect human gestures, and to guide them through shopping (Gai et al, 2013). Additionally, HRs are being developed for space exploration to increase the success of space missions (Fong et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Learning to balance an NAO robot using reinforcement learning with symbolic inverse kinematic

Tutsoy

Barkana

Çolak

2016

Transactions of the Institute of Measurement and Control

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An autonomous humanoid robot (HR) with learning and control algorithms is able to balance itself during sitting down, standing up, walking and running operations, as humans do. In this study, reinforcement learning (RL) with a complete symbolic inverse kinematic (IK) solution is developed to balance the full lower body of a three-dimensional (3D) NAO HR which has 12 degrees of freedom. The IK solution converts the lower body trajectories, which are learned by RL, into reference positions for the joints of the NAO robot. This reduces the dimensionality of the learning and control problems since the IK integrated with the RL eliminates the need to use whole HR states. The IK solution in 3D space takes into account not only the legs but also the full lower body; hence, it is possible to incorporate the effect of the foot and hip lengths on the IK solution. The accuracy and capability of following real joint states are evaluated in the simulation environment. MapleSim is used to model the full lower body, and the developed RL is combined with this model by utilizing Modelica and Maple software properties. The results of the simulation show that the value function is maximized, temporal difference error is reduced to zero, the lower body is stabilized at the upright, and the convergence speed of the RL is improved with use of the symbolic IK solution.

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Mobile shopping cart application using kinect

Cited by 10 publications

References 7 publications

A Combined Method of Skin-and Depth-based Hand Gesture Recognition

A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Human-robot collaboration: A survey

Learning to balance an NAO robot using reinforcement learning with symbolic inverse kinematic

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