Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.(undefined

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or controlling devices. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. 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. Experiments carried out showed that the system was able to achieve an accuracy of 99.4% in terms of hand posture recognition and an average accuracy of 93.72% in terms of dynamic gesture recognition. To validate the proposed framework, two applications were implemented. The first one is a real-time system able to help a robotic soccer referee judge a game in real time. The prototype combines a vision-based hand gesture recognition system with a formal language definition, the Referee CommLang, into what is called the Referee Command Language Interface System (ReCLIS). The second one is a real-time system able to interpret the Portuguese Sign Language. Sign languages are not standard and universal and the grammars differ from country to country. Although the implemented prototype was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.(undefined

Vision-based gesture recognition system for human-computer interaction

Hand gesture recognition, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. This work intends to study and implement a solution, generic enough, able to interpret user commands, composed of a set of dynamic and static gestures, and use those solutions to build an application able to work in a realtime human-computer interaction systems. The proposed solution is composed of two modules controlled by a FSM (Finite State Machine): a real time hand tracking and feature extraction system, supported by a SVM (Support Vector Machine) model for static hand posture classification and a set of HMMs (Hidden Markov Models) for dynamic single stroke hand gesture recognition. The experimental results showed that the system works very reliably, being able to recognize the set of defined commands in real-time. The SVM model for hand posture classification, trained with the selected hand features, achieved an accuracy of 99,2%. The proposed solution as the advantage of being computationally simple to train and use, and at the same time generic enough, allowing its application in any robot/system command interface

Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals

Emotion capture based on body postures and movements

In this paper we present a preliminary study for designing interactive systems that are sensible to human emotions based on the body movements. To do so, we first review the literature on the various approaches for defining and characterizing human emotions. After justifying the adopted characterization space for emotions, we then focus on the movement characteristics that must be captured by the system for being able to recognize the human emotions.Comment: 22 page