Real-time Hand Tracking Using Kinect

Xi, Chenxuan; Chen, Jianxin; Zhao, Chenxue; Pei, Qicheng; Liu, Lizheng

doi:10.1145/3193025.3193056

Cited by 21 publications

(16 citation statements)

References 10 publications

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“…Hand segmentation using the depth sensor of the Kinect camera, followed by location of the fingertips using 3D connections, Euclidean distance, and geodesic distance over hand skeleton pixels to provide increased accuracy was proposed in [ 58 ]. A new 3D hand gesture recognition approach based on a deep learning model using parallel convolutional neural networks (CNN) to process hand skeleton joints’ positions was introduced in [ 59 ], the proposed system has a limitation where it works only with complete sequence.…”

Section: Hand Gesture Methodsmentioning

confidence: 99%

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

2020

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Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

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Section: Hand Gesture Methodsmentioning

confidence: 99%

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

2020

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“…Finally, the kNN algorithm was used with Euclidian distance for finger classification. Another study by Xi et al [18] used a skeleton tracking method to capture the hand and locate fingertips, where a Kalman filter was used to record the motion of the tracked joint. The cascade extraction technique was used with a novel recursive connected component algorithm.…”

Section: Related Workmentioning

confidence: 99%

Elderly Care Based on Hand Gestures Using Kinect Sensor

2020

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Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.

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“…In another study Li et al [7], a depth threshold was used to segment the hand, and then a K-mean algorithm was applied to obtain pixels from both of the user's hands . Another study by Xi et al [8] used a skeleton tracking method to capture the hand and locate fingertips, where the Kalman filter was used to record the motion of the tracked joint. The cascade extraction technique was used with a novel recursive connected component algorithm.…”

Section: Related Workmentioning

confidence: 99%

Computer Vision for Elderly Care Based on Hand Gestures

Oudah¹,

Al-Naji²,

Chahl³

2020

Preprint

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Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

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Real-time Hand Tracking Using Kinect

Cited by 21 publications

References 10 publications

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Elderly Care Based on Hand Gestures Using Kinect Sensor

Computer Vision for Elderly Care Based on Hand Gestures

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