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  1. In this research article, vision based hand gesture recognition (HGR) System has been proposed using machine learning.[1]
  2. Our HGR system addresses the complex background problem and also improves the robustness of hand gesture recognition.[1]
  3. A versatile embedded platform for EMG acquisition and gesture recognition.[2]
  4. Gesture recognition by instantaneous surface EMG images.[2]
  5. Pancholi, S. & Joshi, A. M. Electromyography-based hand gesture recognition system for upper limb amputees.[2]
  6. An EMG gesture recognition system with flexible high-density sensors and brain-inspired high-dimensional classifier.[2]
  7. Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos.[3]
  8. The function of a hand gesture recognition system is to identify the type of movement, from a given set of movements, and the instant when that movement is performed.[4]
  9. Gesture recognition systems have multiple applications including sign language translation, bionics, human-machine interaction, gamming, and virtual reality.[4]
  10. For this reason, hand gesture recognition is a problem where many researchers have focused their attention too.[4]
  11. In this context, in this paper, we present a systematic literature review for hand gesture recognition using ma-chine learning and infrared information.[4]
  12. In this work, we present a novel real-time method for hand gesture recognition.[5]
  13. As we know, the vision-based technology of hand gesture recognition is an important part of human-computer interaction (HCI).[5]
  14. It is greatly different from the traditional hardware based methods and can accomplish human-computer interaction through gesture recognition.[5]
  15. Gesture recognition determines the user intent through the recognition of the gesture or movement of the body or body parts.[5]
  16. focuses in the field include emotion recognition from face and hand gesture recognition.[6]
  17. Using the concept of gesture recognition, it is possible to point a finger at this point will move accordingly.[6]
  18. The term gesture recognition has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet, multi-touch gestures, and mouse gesture recognition.[6]
  19. A standard 2D camera can be used for gesture recognition where the resources/environment would not be convenient for other forms of image-based recognition.[6]
  20. It’s not that unrealistic anymore: hand tracking and gesture recognition technologies are penetrating multiple industries.[7]
  21. Keep in mind that hand tracking and gesture recognition are not the same things.[7]
  22. Gesture recognition can also provide better ergonomics for consumer devices.[7]
  23. Real-time hand gesture recognition for computer interactions is just the next step in technological evolution, and it’s ideally suited for today’s consumer landscape.[7]
  24. In this paper, we present an approach for hand gesture recognition by 3D Convolutional Neural Network 3D_CNN and key frames extractor algorithm by the fast neural network.[8]
  25. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time.[9]
  26. This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition.[9]
  27. The gesture recognition market is expected to register a CAGR of over 27.9% during the forecast period, 2020-2025.[10]
  28. The evolution of GUI technology from the use of texts as inputs to the use of gestures as inputs has paved the way for the emergence of gesture recognition technology.[10]
  29. The use of gesture recognition is increasing in various sectors.[10]
  30. One recent development in this area is the interaction of humans with machines, by using hand gesture recognition.[10]
  31. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors.[11]
  32. Hand-gesture recognition is the process of classifying meaningful gestures of the hands and is currently receiving renewed interest.[11]
  33. Hand-gesture recognition systems also target medical applications, where they are detected via bioelectrical signals instead of vision.[11]
  34. In this paper we present a fully-neuromorphic implementation of sensor fusion for hand-gesture recognition.[11]
  35. AI gesture recognition systems that were initially visual-only have been improved upon by integrating inputs from wearable sensors, an approach known as 'data fusion'.[12]
  36. Contains all the GRT context modules, these are modules that can be connected to a gesture recognition pipeline to input additional context to a real-time classification system.[13]
  37. Gesture recognition and identification of pose in dynamic environment is done in Python language.[14]
  38. Further use of database for hand gesture recognition and their uses are provided in the chapter for real-time implementation on the system to increase the variability use of gesture recognition.[14]
  39. BMW’s Series 7 introduced gesture recognition capabilities in 2016.[15]
  40. Gesture recognition involves integrating multiple elements.[15]
  41. The field of computerized hand-gesture recognition emerged in the early 1980s with the development of wired gloves that integrated sensors on the finger joints, called data gloves.[15]
  42. (Step 1) is perhaps the easiest aspect of making gesture recognition systems work.[15]
  43. Static Hand Gesture Recognition Using Multi-Layer Neural Network Classifier on Hybrid of Features, American Journal of Intelligent Systems, Vol.[16]
  44. Such include Rahman and Afrin (2013) who worked on hand gesture recognition using multi-class support vector machine.[16]
  45. (2012) present a hand gesture recognition based on multi-feature fusion.[16]
  46. Nagarajan and Subashini (2013) developed a static hand gesture recognition system for sign language alphabets using Edge Oriented Histogram and multi-class SVM.[16]
  47. But what then seemed like a pie in the sky, is totally achievable with today’s gesture recognition technology.[17]
  48. The new challenge in gesture recognition is how to get rid of gloves altogether.[17]
  49. But gesture recognition has always been more challenging and only recently started to gain popularity.[17]
  50. Gesture recognition should bring great results no matter the background: it should work whether you’re in the car, at home, or walking down the street.[17]
  51. Through advanced AI-enabled gesture recognition, retailers can assess the popularity of an item by learning from shoppers’ facial and hand gestures.[18]
  52. The evolution of GUI technology from the use of texts as inputs, to the use of gestures as inputs, has paved the way for the emergence of gesture recognition technology.[19]
  53. The gesture recognition usage is increasing, in various sectors.[19]
  54. One recent development in this area is the humans interacting with machines, by using hand gesture recognition.[19]
  55. A gesture recognition application system comprises several key hardware and software components, all of which must be tightly integrated, to provide a compelling user experience.[19]
  56. The design of this efficient human-computer interface for hand gesture recognition is a great area of research which has created employment opportunities for both developers and users.[20]
  57. Finally, the designed model stands out as a measure to suggesting alternative resection method in hand gesture recognition saga.[20]
  58. It is possible to make a gesture recognition system for these people.[21]
  59. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low‐cost and self‐powered interface for gesture recognition.[22]
  60. Differentiated from the abovementioned works, low‐cost and self‐powered glove interfaces based on TENG are developed for complex gesture recognition with the help of machine learning.[22]
  61. Finally, 3D VR/AR applications including gun shooting, baseball pitching, and floral arrangement are achieved by gesture recognition based on the glove‐based HMI.[22]
  62. By such a demonstration, it is proved that the simple gesture recognition can be a choice for two or more states control.[22]

소스

  1. ↑ 1.0 1.1 Development of hand gesture recognition system using machine learning
  2. ↑ 2.0 2.1 2.2 2.3 A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition
  3. ↑ Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors
  4. ↑ 4.0 4.1 4.2 4.3 A Survey on Hand Gesture Recognition Using Machine Learning and Infrared Information
  5. ↑ 5.0 5.1 5.2 5.3 Real-Time Hand Gesture Recognition Using Finger Segmentation
  6. ↑ 6.0 6.1 6.2 6.3 Gesture recognition
  7. ↑ 7.0 7.1 7.2 7.3 Hand Tracking and Gesture Recognition Using AI: Applications and Challenges
  8. ↑ A Real-time Multimodal Hand Gesture Recognition via 3D Convolutional Neural Network and Key Frame Extraction
  9. ↑ 9.0 9.1 Hand Gesture Recognition System Based in Computer Vision and Machine Learning
  10. ↑ 10.0 10.1 10.2 10.3 Gesture Recognition Market Size, Share, Trends, Companies
  11. ↑ 11.0 11.1 11.2 11.3 Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing
  12. ↑ AI system for high precision recognition of hand gestures
  13. ↑ nickgillian/grt: gesture recognition toolkit
  14. ↑ 14.0 14.1 7. Machine vision for human–machine interaction using hand gesture recognition
  15. ↑ 15.0 15.1 15.2 15.3 With a Wave of Your Hand: 3D Gesture Recognition Systems
  16. ↑ 16.0 16.1 16.2 16.3 Static Hand Gesture Recognition Using Multi-Layer Neural Network Classifier on Hybrid of Features
  17. ↑ 17.0 17.1 17.2 17.3 The challenges and opportunities of gesture recognition
  18. ↑ How does AI recognise your hand signs, gestures and movements?
  19. ↑ 19.0 19.1 19.2 19.3 Global Gesture Recognition Market Report 2019-2024: Market is Expected to Register a CAGR of Over 27.9%
  20. ↑ 20.0 20.1 Impact of machine learning techniques on hand gesture recognition
  21. ↑ (PDF) Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
  22. ↑ 22.0 22.1 22.2 22.3 Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications

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