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Proceedings ArticleDOI

Smart device based gesture controller for industrial applications

01 Feb 2017-pp 463-467
TL;DR: The controlling & mechanism to control the industrial machines using android-device's direction motion exploitation on user with a Bluetooth because the liaison is done by sending the accelerometer-sensor data changes to the system via a wireless network.
Abstract: Sensors and network association devices are typically embedded within the smart-phones to support its performance. The Accelerometer-Sensor one among the various sensors embedded in Smart-Devices. Each swinging movement or rotation are going to be scan by the measuring system detector, then, is employed to alter the orientation of the phone show. On the opposite hand, Bluetooth could be a wireless device that is a method of exchanging information between one phone and another phone or between the phone and another device, one among that could be a mechanism. This study discusses the controlling & mechanism to control the industrial machines using android-device's direction motion exploitation on user with a Bluetooth because the liaison. The controlling of the industrial machine's movement is done by sending the accelerometer-sensor data changes to the system via a wireless network.
Citations
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Journal ArticleDOI
TL;DR: A User Customized Smart Keyboard has been developed using SPD-FEAP to adopt fast-changing tends and user requirements that can be visually verified.
Abstract: In a hyper-connected society, IoT environment, markets are rapidly changing as smartphones penetrate global market. As smartphones are applied to various digital media, development of a novel smart product is required. In this paper, a Smart Product Design-Finite Element Analysis Process (SPD-FEAP) is developed to adopt fast-changing tends and user requirements that can be visually verified. The user requirements are derived and quantitatively evaluated from Smart Quality Function Deployment (SQFD) using WebData. Then the usage scenarios are created according to the priority of the functions derived from SQFD. 3D shape analysis by Finite Element Analysis (FEA) was conducted and printed out through Rapid Prototyping (RP) technology to identify any possible errors. Thus, a User Customized Smart Keyboard has been developed using SPD-FEAP.

10 citations

References
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Journal ArticleDOI
TL;DR: A fraction of the recycle slurry is treated with sulphuric acid to convert at least some of the gypsum to calcium sulphate hemihydrate and the slurry comprising hemihYDrate is returned to contact the mixture of phosphate rock, phosphoric acid and recycle Gypsum slurry.
Abstract: The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient "purposive" approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of human-computer interaction.

1,973 citations

Journal ArticleDOI
01 May 2007
TL;DR: A survey on gesture recognition with particular emphasis on hand gestures and facial expressions is provided, and applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail.
Abstract: Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted

1,797 citations

Proceedings ArticleDOI
09 Mar 2009
TL;DR: This work evaluates uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research and shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples.
Abstract: The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

717 citations

Journal ArticleDOI
TL;DR: An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition, which includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features.
Abstract: An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition. It includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features. A Bayes linear classifier and an improved dynamic time-warping algorithm are utilized in the framework. In addition, a prototype system, including a wearable gesture sensing device (embedded with a three-axis accelerometer and four SEMG sensors) and an application program with the proposed algorithmic framework for a mobile phone, is developed to realize gesture-based real-time interaction. With the device worn on the forearm, the user is able to manipulate a mobile phone using 19 predefined gestures or even personalized ones. Results suggest that the developed prototype responded to each gesture instruction within 300 ms on the mobile phone, with the average accuracy of 95.0% in user-dependent testing and 89.6% in user-independent testing. Such performance during the interaction testing, along with positive user experience questionnaire feedback, demonstrates the utility of the framework.

249 citations

Journal ArticleDOI
01 Feb 2001
TL;DR: A complete vision-based system, consisting of hand gesture acquisition, segmentation, filtering, representation and classification, is developed to robustly classify hand gestures and it is estimated that real-time gesture classification is possible through the use of a high- Speed PC, high-speed digital signal processing chips and code optimization.
Abstract: The accurate classification of hand gestures is crucial in the development of novel hand gesture-based systems designed for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC). A complete vision-based system, consisting of hand gesture acquisition, segmentation, filtering, representation and classification, is developed to robustly classify hand gestures. The algorithms in the subsystems are formulated or selected to optimality classify hand gestures. The gray-scale image of a hand gesture is segmented using a histogram thresholding algorithm. A morphological filtering approach is designed to effectively remove background and object noise in the segmented image. The contour of a gesture is represented by a localized contour sequence whose samples are the perpendicular distances between the contour pixels and the chord connecting the end-points of a window centered on the contour pixels. Gesture similarity is determined by measuring the similarity between the localized contour sequences of the gestures. Linear alignment and nonlinear alignment are developed to measure the similarity between the localized contour sequences. Experiments and evaluations on a subset of American Sign Language (ASL) hand gestures show that, by using nonlinear alignment, no gestures are misclassified by the system. Additionally, it is also estimated that real-time gesture classification is possible through the use of a high-speed PC, high-speed digital signal processing chips and code optimization.

119 citations