Kinect based calling gesture recognition for taking order service of elderly care robot

Zhao, Xinshuang; Naguib, Ahmed; Lee, Sukhan

doi:10.1109/roman.2014.6926306

Cited by 27 publications

(13 citation statements)

References 6 publications

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“…Another direction of research is associated with recognition of gestures performed by entire body [8,9], with some attention to assistive technology [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

Ribó¹,

Warchoł²,

Oszust³

2016

IJISA

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Gestures are natural means of co mmun ication between humans, and therefore their application would benefit to many fields where usage of typical input devices, such as keyboards or joysticks is cu mbersome or unpractical (e.g., in noisy environ ment). Recently, together with emergence of new cameras that allow obtaining not only colour images of observed scene, but also offer the software developer rich informat ion on the number of seen hu mans and, what is most interesting, 3D positions of their body parts, practical applications using body gestures have become more popular. Such informat ion is presented in a form o f skeletal data. In this paper, an approach to gesture recognition bas ed on skeletal data using nearest neighbour classifier with dynamic time warping is presented. Since similar approaches are widely used in the literature, a few practical improvements that led to better recognition results are proposed. The approach is extensively evaluated on three publicly availab le gesture datasets and compared with state-of-the-art classifiers. For some gesture datasets, the proposed approach outperformed its competitors in terms of recognition rate and time of recognition.

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“…Another direction of research is associated with recognition of gestures performed by entire body [8,9], with some attention to assistive technology [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

Ribó¹,

Warchoł²,

Oszust³

2016

IJISA

View full text Add to dashboard Cite

show abstract

“…Zhao et al [17] show a calling gesture recognition for taking order service of an elderly care robot. It was designed mainly for helping non-expert users like elderly to call a service robot.…”

Section: Gesture Recognition With Kinect For the Control Of Mobile Romentioning

confidence: 99%

Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot

Pfitscher¹,

Welfer²,

Nascimento³

et al. 2019

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In this paper, we use data from the Microsoft Kinect sensor that processes the captured image of a person using and extracting the joints information on every frame. Then, we propose the creation of an image derived from all the sequential frames of a gesture the movement, which facilitates training in a convolutional neural network. We trained a CNN using two strategies: combined training and individual training. The strategies were experimented in the convolutional neural network (CNN) using the MSRC-12 dataset, obtaining an accuracy rate of 86.67% in combined training and 90.78% of accuracy rate in the individual training. Then, the trained neural network was used to classify data obtained from Kinect with a person, obtaining an accuracy rate of 72.08% in combined training and 81.25% in individualized training. Finally, we use the system to send commands to a mobile robot in order to control it.

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“…Many smart home care concepts (2) have been proposed; some use wheeled or even humanoid robots. (3)(4)(5) Of these, remote care needs more attention. A remote care system involves people who receive care, those who give care, and family members.…”

Section: Introductionmentioning

confidence: 99%

Realization of Person Tracking and Gesture Recognition with a Quadrotor System

Pai¹,

Zhou²,

Chen³

et al. 2019

Sensors and Materials

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In this paper, the design of a quadrotor vehicle having a person-tracking and observation system, which uses human gesture recognition, is described. The system has three operating functions, namely, object tracking, human gesture recognition, and fixed-point cruising. The tracking-learning-detection (TLD) algorithm was used to enable the autonomous tracking of the object from images. An extended Kalman filter (EKF) provides an estimate of the current position of the quadrotor vehicle, and a fuzzy-proportional integral derivative (PID) controller provides position error compensation. The principle of the human gesture recognition system is as follows. A background model is first built from images using a Gaussian mixture model (GMM) to detect the foreground image. A nonlinear support vector machine (SVM) is then employed to recognize changes of gesture and establish interactivity between the vehicle and the user. The coordinates of the vehicle are marked using a GPS for fixed-point cruising. The coordinates and parameters of the points are set so that the quadrotor vehicle can follow them during cruising. Lastly, all of the functions are incorporated into the person-tracking and gesture-recognition system in the quadrotor. The experimental results show the feasibility of the above-mentioned methods, which can help us easily recognize the various gestures in this study.

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Kinect based calling gesture recognition for taking order service of elderly care robot

Cited by 27 publications

References 6 publications

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

An Approach to Gesture Recognition with Skeletal Data Using Dynamic Time Warping and Nearest Neighbour Classifier

Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot

Realization of Person Tracking and Gesture Recognition with a Quadrotor System

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