Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks
New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the noncommunicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.
Code (0)
등록된 구현이 없습니다.
Tasks
Gesture RecognitionSimilar Papers 제목 키워드 기반
Continuous and Simultaneous Gesture and Posture Recognition for Commanding a Robotic Wheelchair; Towards Spotting the Signal Patterns
Spotting signal patterns with varying lengths has been still an open problem in the literature. In this study, we describe a signal pattern recognition approach for continuous and simultaneous classification of a tracked…
ClusteringGeneral ClassificationSparse Representation-based ClassificationOO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation
Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR. However, many of the methods proposed to recognize heterogeneous han…
ClassificationGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionIPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture sam…
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionOptical Flow Estimation+1Hand gesture recognition using 802.11ad mmWave sensor in the mobile device
We explore the feasibility of AI assisted hand-gesture recognition using 802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information (RDI) is obtained by using pulse Doppler radar for gesture recognition…
Data AugmentationGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionReal-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based g…
Action DetectionActivity DetectionEdge-computingGesture Recognition