Hand Gesture Recognition and Interface via a Depth Imaging Sensor for Smart Home Appliances

Dinh, Dong-Luong; Kim, Jeong Tai; Kim, Tae‐Seong

doi:10.1016/j.egypro.2014.12.419

Cited by 43 publications

(18 citation statements)

References 2 publications

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“… Controlling a music recording (Lee et al, 2006),  Game control (Carbini et al, 2006, Bannach et al, 2007, Kratz et al, 2007, Xu et al, 2009, Chen et al, 2011, Roccetti et al, 2012, Sodhi et al, 2013, Dardas et al, 2014, Lv et al, 2015, Trigueiros et al, 2015, Yeo et al, 2015,  Home appliance control (Kela et al, 2006, Liu et al, 2009, Schreiber et al, 2009, Chen et al, 2010, Pan et al, 2010, Boulabiar et al, 2011, Garzotto and Valoriani, 2013, Hoste and Signer, 2013, Takahashi et al, 2013, Dinh et al, 2014, Denkowski et al, 2015, Zaii et al, 2015, Wu et al, 2016,  Interaction with car controls (Mahr et al, 2011, Kajastila and Lokki, 2013, Riener et al, 2013, Lauber et al, 2014, Buddhikot et al, 2018,  Robot control (Pook and Ballard, 1996, Savage-Carmona et al, 1998, Waldherr et al, 2000, Fong et al, 2001, Rogalla et al, 2002, Hasanuzzaman et al, 2006, Kim et al, 2008, Van Den Bergh et al, 2011, Xian et al, 2012…”

Section: Touchless Controlmentioning

confidence: 99%

Systematic literature review of hand gestures used in human computer interaction interfaces

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

138

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Section: Touchless Controlmentioning

confidence: 99%

Systematic literature review of hand gestures used in human computer interaction interfaces

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

138

View full text Add to dashboard Cite

“…where p gt M depends on the ground truth factor, and p pred M is the prediction factor of the network [5]. The error function for the third network, which transfers 2D information to 3D is calculated by:…”

Section: Transferring 2d Information To 3d and Estimating 3d Hand Pomentioning

confidence: 99%

“…The process of hand posture recognition is performed by machine-vision and image processing. There are two approaches for hand posture recognition; sensor-based and vision-based methods [5]. In the first approach, particular sensors are used, and users may need to wear the glove or may use the web camera for capturing the hand image.…”

Section: Introduction *mentioning

confidence: 99%

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Gheitasi

Farsi

Mohamadzadeh

2020

IJE

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Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. To simplify the proposed method and to be more functional, the depth factor is ignored. So only the simple color images of hands are used as inputs of the system. The proposed method is evaluated by using two datasets with high-diversity named Mixamo and RWTH, which include 43,986 and 1160 color images, respectively, where 74% of these images are selected as a training set and, 26% of the rest images are selected as the evaluation set. The experiments show that the proposed method provides better results in both hand posture recognition and detection of sign languages compared to state-of-the-art methods.

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“…Specifically, gesture recognition is very suitable for future applications such as smart home and virtual reality. Numerous applications [8][9][10][11][12] have been proposed for smart home and virtual reality. It is more convenient and relaxing for people to interact with machines using gestures, thus making gestures a great potential for the future HCI technology.…”

Section: Introductionmentioning

confidence: 99%

Towards Location Independent Gesture Recognition with Commodity WiFi Devices

Wang

2019

Electronics

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Recently, WiFi-based gesture recognition has attracted increasing attention. Due to the sensitivity of WiFi signals to environments, an activity recognition model trained at a specific place can hardly work well for other places. To tackle this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Leveraging the low rank and sparse decomposition, WiHand separates gesture signal from background information, thus making it resilient to location variation. Extensive evaluations showed that WiHand can achieve an average accuracy of 93% for various locations. In addition, WiHand works well under through the wall scenario.

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Hand Gesture Recognition and Interface via a Depth Imaging Sensor for Smart Home Appliances

Cited by 43 publications

References 2 publications

Systematic literature review of hand gestures used in human computer interaction interfaces

Systematic literature review of hand gestures used in human computer interaction interfaces

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Towards Location Independent Gesture Recognition with Commodity WiFi Devices

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