Relevant bibliographies by topics / Gesture recognition module

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Academic literature on the topic 'Gesture recognition module'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Gesture recognition module"

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Chen, Shang-Liang, and Li-Wu Huang. "Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition." International Journal of Engineering and Technology Innovation 11, no. 4 (2021): 241–50. http://dx.doi.org/10.46604/ijeti.2021.7342.

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In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and executed by the robot arm control module. With the proposed program, engineers can interact with the robot arm through hand gestures, teach the robot arm to record the trajectory by simple hand movements, and call different scripts to satisfy robot motion requirements in the actual production environment.
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Nguyen, Ngoc-Hoang, Tran-Dac-Thinh Phan, Soo-Hyung Kim, Hyung-Jeong Yang, and Guee-Sang Lee. "3D Skeletal Joints-Based Hand Gesture Spotting and Classification." Applied Sciences 11, no. 10 (2021): 4689. http://dx.doi.org/10.3390/app11104689.

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This paper presents a novel approach to continuous dynamic hand gesture recognition. Our approach contains two main modules: gesture spotting and gesture classification. Firstly, the gesture spotting module pre-segments the video sequence with continuous gestures into isolated gestures. Secondly, the gesture classification module identifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and fingers are fed into the Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting. In the gesture classification module, three residual 3D Convolution Neural Networks based on ResNet architectures (3D_ResNet) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the multiple data channels such as RGB, Optical Flow, Depth, and 3D positions of key joints. The promising performance of our approach is obtained through experiments conducted on three public datasets—Chalearn LAP ConGD dataset, 20BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach outperforms the state-of-the-art methods on the Chalearn LAP ConGD dataset.
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Min, Huasong, Ziming Chen, Bin Fang, et al. "Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks." Scientific Programming 2021 (July 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/6680417.

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Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.
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Bhargavi, Mrs Jangam, Chitikala Sairam, and Donga Hemanth. "Real time interface for deaf-hearing communication." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02356.

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Bridging the communication gap between the deaf and hearing communities using AI is achieved by integrating two key modules: Speech-to-Sign Language Translation and Sign Gesture Detection in Real Time. The first module translates English spoken language into American Sign Language (ASL) animations. It consists of three sub-modules: speech-to-text conversion using the speech recognition module in Python, English text to ASL gloss translation using an NLP model, and ASL gloss to animated video generation, where DWpose Pose Estimation, and an avatar is used for visual representation. The second module focuses on real-time sign gesture detection, where a dataset is created from the WLASL and MS-ASL datasets. Hand gestures are labeled using Labeling, and a YOLO-based model is trained for hand pose detection to enable real-time recognition. The system aims to enhance accessibility and interaction between deaf and hearing users through an efficient, automated translation and recognition pipeline. Keywords: Speech-to-sign translation, real-time sign language recognition, ASL gloss, YOLO hand pose detection, AI for accessibility, deep learning for sign language, gesture recognition, DWpose Pose Estimation, NLP, dataset labeling, real-time gesture recognition.
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Tao, Chongben, and Guodong Liu. "A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/384865.

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To achieve Human-Robot Interaction (HRI) by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs) is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs) and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs) to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs) is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance.
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R N, Pushpa. "Sign Language Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 565–71. https://doi.org/10.22214/ijraset.2024.65817.

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Sign language recognition is an essential tool for bridging communication gaps between individuals with hearing or speech impairments and the broader community. This study introduces an advanced sign language recognition system leveraging computer vision and machine learning techniques. The system utilizes real-time hand tracking and gesture recognition to identify and classify hand gestures associated with common phrases such as "Hello," "I love you," and "Thank you." A two-step approach is implemented: first, a data collection module captures hand images using a robust preprocessing pipeline, ensuring uniformity in image size and quality; second, a classification module uses a trained deep learning model to accurately predict gestures in real-time. The framework integrates OpenCV for image processing, CVZone modules for hand detection, and TensorFlow for gesture classification. Extensive testing demonstrates the system's capability to process live video input, classify gestures accurately, and display corresponding labels seamlessly. This solution addresses challenges in gesture recognition, such as variable hand shapes and dynamic backgrounds, through efficient preprocessing and model training. By offering a scalable and efficient design, this work has the potential to contribute significantly to assistive technologies and accessible communication systems, paving the way for further advancements in human-computer interaction and inclusive technology.
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Chen, Zengzhao, Wenkai Huang, Hai Liu, Zhuo Wang, Yuqun Wen, and Shengming Wang. "ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition." Sensors 24, no. 8 (2024): 2589. http://dx.doi.org/10.3390/s24082589.

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Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed.
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Ahmed, Shahzad, and Sung Ho Cho. "Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier." Sensors 20, no. 2 (2020): 564. http://dx.doi.org/10.3390/s20020564.

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The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.
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Wang, Zhiyuan, Chongyuan Bi, Songhui You, and Junjie Yao. "Hidden Markov Model-Based Video Recognition for Sports." Advances in Mathematical Physics 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/5183088.

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In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.
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Jing, Dong-Xing, Kui Huang, Shi-Jian Liu, Zheng Zou, and Chih-Yu Hsu. "Dynamic Hypergraph Convolutional Networks for Hand Motion Gesture Sequence Recognition." Technologies 13, no. 6 (2025): 257. https://doi.org/10.3390/technologies13060257.

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This paper introduces a novel approach to hand motion gesture recognition by integrating the Fourier transform with hypergraph convolutional networks (HGCNs). Traditional recognition methods often struggle to capture the complex spatiotemporal dynamics of hand gestures. HGCNs, which are capable of modeling intricate relationships among joints, are enhanced by Fourier transform to analyze gesture features in the frequency domain. A hypergraph is constructed to represent the interdependencies among hand joints, allowing for dynamic adjustments based on joint movements. Hypergraph convolution is applied to update node features, while the Fourier transform facilitates frequency-domain analysis. The T-Module, a multiscale temporal convolution module, aggregates features from multiple frames to capture gesture dynamics across different time scales. Experiments on the dynamic hypergraph (DHG14/28) and shape retrieval contest (SHREC’17) datasets demonstrate the effectiveness of the proposed method, achieving accuracies of 96.4% and 97.6%, respectively, and outperforming traditional gesture recognition algorithms. Ablation studies further validate the contributions of each component in enhancing recognition performance.
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Dissertations / Theses on the topic "Gesture recognition module"

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Terzi, Matteo. "Learning interpretable representations for classification, anomaly detection, human gesture and action recognition." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3423183.

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The goal of this thesis is to provide algorithms and models for classification, gesture recognition and anomaly detection with a partial focus on human activity. In applications where humans are involved, it is of paramount importance to provide robust and understandable algorithms and models. A way to accomplish this requirement is to use relatively simple and robust approaches, especially when devices are resource-constrained. The second approach, when a large amount of data is present, is to adopt complex algorithms and models and make them robust and interpretable from a human-like point of view. This motivates our thesis that is divided in two parts. The first part of this thesis is devoted to the development of parsimonious algorithms for action/gesture recognition in human-centric applications such as sports and anomaly detection for artificial pancreas. The data sources employed for the validation of our approaches consist of a collection of time-series data coming from sensors, such as accelerometers or glycemic. The main challenge in this context is to discard (i.e. being invariant to) many nuisance factors that make the recognition task difficult, especially where many different users are involved. Moreover, in some cases, data cannot be easily labelled, making supervised approaches not viable. Thus, we present the mathematical tools and the background with a focus to the recognition problems and then we derive novel methods for: (i) gesture/action recognition using sparse representations for a sport application; (ii) gesture/action recognition using a symbolic representations and its extension to the multivariate case; (iii) model-free and unsupervised anomaly detection for detecting faults on artificial pancreas. These algorithms are well-suited to be deployed in resource constrained devices, such as wearables. In the second part, we investigate the feasibility of deep learning frameworks where human interpretation is crucial. Standard deep learning models are not robust and, unfortunately, literature approaches that ensure robustness are typically detrimental to accuracy in general. However, in general, real-world applications often require a minimum amount of accuracy to be employed. In view of this, after reviewing some results present in the recent literature, we formulate a new algorithm being able to semantically trade-off between accuracy and robustness, where a cost-sensitive classification problem is provided and a given threshold of accuracy is required. In addition, we provide a link between robustness to input perturbations and interpretability guided by a physical minimum energy principle: in fact, leveraging optimal transport tools, we show that robust training is connected to the optimal transport problem. Thanks to these theoretical insights we develop a new algorithm that provides robust, interpretable and more transferable representations.
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Niezen, Gerrit. "The optimization of gesture recognition techniques for resource-constrained devices." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01262009-125121/.

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Rajah, Christopher. "Chereme-based recognition of isolated, dynamic gestures from South African sign language with Hidden Markov Models." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_4979_1183461652.

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<p>Much work has been done in building systems that can recognize gestures, e.g. as a component of sign language recognition systems. These systems typically use whole gestures as the smallest unit for recognition. Although high recognition rates have been reported, these systems do not scale well and are computationally intensive. The reason why these systems generally scale poorly is that they recognize gestures by building individual models for each separate gesture<br>as the number of gestures grows, so does the required number of models. Beyond a certain threshold number of gestures to be recognized, this approach become infeasible. This work proposed that similarly good recognition rates can be achieved by building models for subcomponents of whole gestures, so-called cheremes. Instead of building models for entire gestures, we build models for cheremes and recognize gestures as sequences of such cheremes. The assumption is that many gestures share cheremes and that the number of cheremes necessary to describe gestures is much smaller than the number of gestures. This small number of cheremes then makes it possible to recognized a large number of gestures with a small number of chereme models. This approach is akin to phoneme-based speech recognition systems where utterances are recognized as phonemes which in turn are combined into words.</p>
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Ma, Limin. "Statistical Modeling of Video Event Mining." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1146792818.

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Yang, Ruiduo. "Dynamic programming with multiple candidates and its applications to sign language and hand gesture recognition." [Tampa, Fla.] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002310.

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Bodiroža, Saša. "Gestures in human-robot interaction." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17705.

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Gesten sind ein Kommunikationsweg, der einem Betrachter Informationen oder Absichten übermittelt. Daher können sie effektiv in der Mensch-Roboter-Interaktion, oder in der Mensch-Maschine-Interaktion allgemein, verwendet werden. Sie stellen eine Möglichkeit für einen Roboter oder eine Maschine dar, um eine Bedeutung abzuleiten. Um Gesten intuitiv benutzen zukönnen und Gesten, die von Robotern ausgeführt werden, zu verstehen, ist es notwendig, Zuordnungen zwischen Gesten und den damit verbundenen Bedeutungen zu definieren -- ein Gestenvokabular. Ein Menschgestenvokabular definiert welche Gesten ein Personenkreis intuitiv verwendet, um Informationen zu übermitteln. Ein Robotergestenvokabular zeigt welche Robotergesten zu welcher Bedeutung passen. Ihre effektive und intuitive Benutzung hängt von Gestenerkennung ab, das heißt von der Klassifizierung der Körperbewegung in diskrete Gestenklassen durch die Verwendung von Mustererkennung und maschinellem Lernen. Die vorliegende Dissertation befasst sich mit beiden Forschungsbereichen. Als eine Voraussetzung für die intuitive Mensch-Roboter-Interaktion wird zunächst ein Aufmerksamkeitsmodell für humanoide Roboter entwickelt. Danach wird ein Verfahren für die Festlegung von Gestenvokabulare vorgelegt, das auf Beobachtungen von Benutzern und Umfragen beruht. Anschliessend werden experimentelle Ergebnisse vorgestellt. Eine Methode zur Verfeinerung der Robotergesten wird entwickelt, die auf interaktiven genetischen Algorithmen basiert. Ein robuster und performanter Gestenerkennungsalgorithmus wird entwickelt, der auf Dynamic Time Warping basiert, und sich durch die Verwendung von One-Shot-Learning auszeichnet, das heißt durch die Verwendung einer geringen Anzahl von Trainingsgesten. Der Algorithmus kann in realen Szenarien verwendet werden, womit er den Einfluss von Umweltbedingungen und Gesteneigenschaften, senkt. Schließlich wird eine Methode für das Lernen der Beziehungen zwischen Selbstbewegung und Zeigegesten vorgestellt.<br>Gestures consist of movements of body parts and are a mean of communication that conveys information or intentions to an observer. Therefore, they can be effectively used in human-robot interaction, or in general in human-machine interaction, as a way for a robot or a machine to infer a meaning. In order for people to intuitively use gestures and understand robot gestures, it is necessary to define mappings between gestures and their associated meanings -- a gesture vocabulary. Human gesture vocabulary defines which gestures a group of people would intuitively use to convey information, while robot gesture vocabulary displays which robot gestures are deemed as fitting for a particular meaning. Effective use of vocabularies depends on techniques for gesture recognition, which considers classification of body motion into discrete gesture classes, relying on pattern recognition and machine learning. This thesis addresses both research areas, presenting development of gesture vocabularies as well as gesture recognition techniques, focusing on hand and arm gestures. Attentional models for humanoid robots were developed as a prerequisite for human-robot interaction and a precursor to gesture recognition. A method for defining gesture vocabularies for humans and robots, based on user observations and surveys, is explained and experimental results are presented. As a result of the robot gesture vocabulary experiment, an evolutionary-based approach for refinement of robot gestures is introduced, based on interactive genetic algorithms. A robust and well-performing gesture recognition algorithm based on dynamic time warping has been developed. Most importantly, it employs one-shot learning, meaning that it can be trained using a low number of training samples and employed in real-life scenarios, lowering the effect of environmental constraints and gesture features. Finally, an approach for learning a relation between self-motion and pointing gestures is presented.
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Pavllo, Dario. "Riconoscimento real-time di gesture tramite tecniche di machine learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10999/.

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Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.
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Gurrapu, Chaitanya. "Human Action Recognition In Video Data For Surveillance Applications." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15878/1/Chaitanya_Gurrapu_Thesis.pdf.

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Detecting human actions using a camera has many possible applications in the security industry. When a human performs an action, his/her body goes through a signature sequence of poses. To detect these pose changes and hence the activities performed, a pattern recogniser needs to be built into the video system. Due to the temporal nature of the patterns, Hidden Markov Models (HMM), used extensively in speech recognition, were investigated. Initially a gesture recognition system was built using novel features. These features were obtained by approximating the contour of the foreground object with a polygon and extracting the polygon's vertices. A Gaussian Mixture Model (GMM) was fit to the vertices obtained from a few frames and the parameters of the GMM itself were used as features for the HMM. A more practical activity detection system using a more sophisticated foreground segmentation algorithm immune to varying lighting conditions and permanent changes to the foreground was then built. The foreground segmentation algorithm models each of the pixel values using clusters and continually uses incoming pixels to update the cluster parameters. Cast shadows were identified and removed by assuming that shadow regions were less likely to produce strong edges in the image than real objects and that this likelihood further decreases after colour segmentation. Colour segmentation itself was performed by clustering together pixel values in the feature space using a gradient ascent algorithm called mean shift. More robust features in the form of mesh features were also obtained by dividing the bounding box of the binarised object into grid elements and calculating the ratio of foreground to background pixels in each of the grid elements. These features were vector quantized to reduce their dimensionality and the resulting symbols presented as features to the HMM to achieve a recognition rate of 62% for an event involving a person writing on a white board. The recognition rate increased to 80% for the &quotseen" person sequences, i.e. the sequences of the person used to train the models. With a fixed lighting position, the lack of a shadow removal subsystem improved the detection rate. This is because of the consistent profile of the shadows in both the training and testing sequences due to the fixed lighting positions. Even with a lower recognition rate, the shadow removal subsystem was considered an indispensable part of a practical, generic surveillance system.
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Gurrapu, Chaitanya. "Human Action Recognition In Video Data For Surveillance Applications." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15878/.

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Detecting human actions using a camera has many possible applications in the security industry. When a human performs an action, his/her body goes through a signature sequence of poses. To detect these pose changes and hence the activities performed, a pattern recogniser needs to be built into the video system. Due to the temporal nature of the patterns, Hidden Markov Models (HMM), used extensively in speech recognition, were investigated. Initially a gesture recognition system was built using novel features. These features were obtained by approximating the contour of the foreground object with a polygon and extracting the polygon's vertices. A Gaussian Mixture Model (GMM) was fit to the vertices obtained from a few frames and the parameters of the GMM itself were used as features for the HMM. A more practical activity detection system using a more sophisticated foreground segmentation algorithm immune to varying lighting conditions and permanent changes to the foreground was then built. The foreground segmentation algorithm models each of the pixel values using clusters and continually uses incoming pixels to update the cluster parameters. Cast shadows were identified and removed by assuming that shadow regions were less likely to produce strong edges in the image than real objects and that this likelihood further decreases after colour segmentation. Colour segmentation itself was performed by clustering together pixel values in the feature space using a gradient ascent algorithm called mean shift. More robust features in the form of mesh features were also obtained by dividing the bounding box of the binarised object into grid elements and calculating the ratio of foreground to background pixels in each of the grid elements. These features were vector quantized to reduce their dimensionality and the resulting symbols presented as features to the HMM to achieve a recognition rate of 62% for an event involving a person writing on a white board. The recognition rate increased to 80% for the &quotseen" person sequences, i.e. the sequences of the person used to train the models. With a fixed lighting position, the lack of a shadow removal subsystem improved the detection rate. This is because of the consistent profile of the shadows in both the training and testing sequences due to the fixed lighting positions. Even with a lower recognition rate, the shadow removal subsystem was considered an indispensable part of a practical, generic surveillance system.
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Jaroň, Lukáš. "Ovládání počítače gesty." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236609.

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This masters thesis describes possibilities and principles of gesture-based computer interface. The work describes general approaches for gesture control.  It also deals with implementation of the selected detection method of the hands and fingers using depth maps loaded form Kinect sensor. The implementation also deals with gesture recognition using hidden Markov models. For demonstration purposes there is also described implementation of a simple photo viewer that uses developed gesture-based computer interface. The work also focuses on quality testing and accuracy evaluation for selected gesture recognizer.
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Books on the topic "Gesture recognition module"

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Mäntylä, Vesa-Matti. Discrete hidden Markov models with application to isolated user-dependent hand gesture recognition. Technical Research Centre of Finland, 2001.

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Book chapters on the topic "Gesture recognition module"

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Lee, Kyung-Taek, Seho Park, and Yong-Suk Park. "Implementation of Stereo Camera Module for Hand Gesture Recognition." In Computer Science and its Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45402-2_7.

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Yoda, Ikushi, Kazuyuki Itoh, and Tsuyoshi Nakayama. "Extended Mouth/Tongue Gesture Recognition Module for People with Severe Motor Dysfunction." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08648-9_42.

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Ramo, Mirco, and Guénolé C. M. Silvestre. "A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_5.

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AbstractThe Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creating latent representations that are correctly decoded to the exact mathematical expression tree, providing robustness to ablated inputs and unseen glyphs. For the first time, the encoder is fed with spatio-temporal data tokens potentially forming an infinitely large vocabulary, which finds applications beyond that of online gesture recognition. A new supervised dataset of online handwriting gestures is provided for training models on generic handwriting recognition tasks and a new metric is proposed for the evaluation of the syntactic correctness of the output expression trees. A small Transformer model suitable for edge inference was successfully trained to an average normalised Levenshtein accuracy of 94%, resulting in valid postfix RPN tree representation for 94% of predictions.
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Tsuruta, Naoyuki, Yuichiro Yoshiki, and Tarek El Tobely. "A Randomized Hypercolumn Model and Gesture Recognition." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_27.

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Vidhya, N., M. Shabana Parveen, G. Valarmathy, et al. "Gesture recognition gloves for sign language translation." In Security Issues in Communication Devices, Networks and Computing Models. CRC Press, 2025. https://doi.org/10.1201/9781003513445-13.

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Riaz, Zahid, Christoph Mayer, Michael Beetz, and Bernd Radig. "Facial Expressions Recognition from Image Sequences." In Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03320-9_29.

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Maskeliunas, Rytis, Algimantas Rudzionis, and Vytautas Rudzionis. "Analysis of the Possibilities to Adapt the Foreign Language Speech Recognition Engines for the Lithuanian Spoken Commands Recognition." In Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03320-9_38.

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Silovsky, Jan, Petr Cerva, and Jindrich Zdansky. "MLLR Transforms Based Speaker Recognition in Broadcast Streams." In Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03320-9_39.

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Sztahó, Dávid, Katalin Nagy, and Klára Vicsi. "Automatic Sentence Modality Recognition in Children’s Speech, and Its Usage Potential in the Speech Therapy." In Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03320-9_25.

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Yoda Ikushi, Ito Kazuyuki, and Nakayama Tsuyoshi. "Modular Gesture Interface for People with Severe Motor Dysfunction: Foot Recognition." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-798-6-725.

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We have collected various gestures from persons with motor dysfunction who cannot use normal interface switches to develop contactless, non-constraining, and inexpensive gesture interfaces for operating PCs by utilizing a commercially available image range sensor. We describe the collection and classification of the gestures and the foot gesture recognition module.
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Conference papers on the topic "Gesture recognition module"

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Udhayakumar, M., Titus Issac, and J. Sebastian Terance. "Investigation of Hand Gesture Recognition and Script Generation Models." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00028.

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Asih, Nurma', Bimo Sunarfri Hantono, and Indriana Hidayah. "Exploring Models Approach for Real-Time Hand Gesture Recognition." In 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). IEEE, 2025. https://doi.org/10.1109/icadeis65852.2025.10933403.

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Qu, Yan, Congsheng Li, and Haoyu Jiang. "Classification and recognition of gesture EEG signals with Transformer-Based models." In 2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC). IEEE, 2024. http://dx.doi.org/10.1109/raiic61787.2024.10670764.

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Ge, Zhaojie, Weiming Liu, Zhile Wu, Mei Cai, and Ping Zhao. "Gesture Recognition and Master-Slave Control of a Manipulator Based on sEMG and CNN-GRU." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94788.

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Abstract Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. In gesture recognition, sEMG is a non-invasive, efficient and fast recognition method. For patients with hand amputation, their upper limb EMG signals can be collected, and these EMG signals correspond to the patient’s hand movement intention. Therefore, by wearing the prosthetic hand integrated with the EMG signal recognition module, patients with hand amputation can also make gestures meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. At the same time, gesture recognition can also be applied to remote control. Controlling the end of the manipulator at a certain distance with a specific gesture can complete some tasks in complex and high-risk environments with higher efficiency. Based on Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU), this paper constructs three neural networks with different structures, including single CNN, single GRU and CNN-GRU, and then train the collected gesture data set. According to the results of test set, the input type with the highest accuracy of gesture classification and recognition can be obtained. Among the three neural networks, CNN-GRU has the highest accuracy on the test set, reaching 92%, so it is used as the selected gesture recognition network. Finally, combined with the integrated manipulator, the EMG signals collected in real time by the myo EMG signal acquisition armband are classified by the upper computer, and the results are obtained. Then the control signal of the manipulator corresponding to the gesture is sent to the Arduino control module of the manipulator, and the master-slave control of the manipulator using the EMG signal can be realized.
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Liu, Zhiqi, and Hua Li. "Dynamic gesture recognition based on temporal shift module." In Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), edited by Xiaoli Li. SPIE, 2023. http://dx.doi.org/10.1117/12.2671435.

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G, Fathima, Mogan Ram G S, Siva Subramanian E, Sasi Kumar V, and Mukunda M. "ISL(Indian Sign Language) Gesture to Speech with Multilingual Support." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/lnzk4758/icrcct24p70.

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The Real Time Indian Sign Language (ISL) Translator project aims to bridge the communication gap between the deaf and hard of hearing community and the hearing world by leveraging advanced deep learning techniques. This project utilizes Convolutional Neural Networks (CNNs) to recognize the ISL gesture and converts them into text. The recognized text is then translated into speech in multiple Indian languages, allowing seamless real time communication. The system is comprised of three primary components: the Gesture Recognition Module, which employs a CNN to process real time video input of ISL gestures; the Text processing Module, which converts the recognized gesture into structured text; and the Text to Speech (TTS) Module, which translates the test into speech in various Indian languages. An additional Language Selection Module provides users with the option to choose their preferred language for the TTS output. To ensure accessibility and usability, this project integrates a user friendly website interface, enabling individuals to use the translator easily across different platform. By fostering greater inclusion and improving communication in social, educational, and professional settings, the Real Time ISL Translator project stands as a significant technological advancement for the deaf and hard of hearing community in India.
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Borrego-Carazo, Juan, David Castells-Rufas, Jordi Carrabina, and Ernesto Biempica. "Capacitive-sensing module with dynamic gesture recognition for automotive applications." In 2020 23rd International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS). IEEE, 2020. http://dx.doi.org/10.1109/ddecs50862.2020.9095748.

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Fang, Qun, YiHui Yan, and GuoQing Ma. "Gesture Recognition in Millimeter-Wave Radar based on Spatio-Temporal Feature Sequences." In 4th International Conference on Machine Learning Techniques and NLP. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131610.

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Gesture recognition is a pivotal technology in the realm of intelligent education, and millimeter-wave (mmWave) signals possess advantages such as high resolution and strong penetration capability. This paper introduces a highly accurate and robust gesture recognition method using mmWave radar. The method involves capturing the raw signals of hand movements with the mmWave radar module and preprocessing the received radar signals, including Fourier transformation, distance compression, Doppler processing, and noise reduction through moving target indication (MTI). The preprocessed signals are then fed into the Convolutional Neural Network-Time Domain Convolutional Network (CNN-TCN) model to extract spatio-temporal features, with recognition performance evaluated through classification. Experimental results demonstrate that this method achieves an accuracy rate of 98.2% in domain-specific recognition and maintains a consistently high recognition rate across different neural networks, showcasing exceptional recognition performance and robustness
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Ge, Yuncheng, Yewei Huang, Ye Julei, Huazixi Zeng, Hechong Su, and Zengyao Yang. "DMGR: Divisible Multi-complex Gesture Recognition Based on Word Segmentation Processing." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005654.

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In the realm of gesture recognition and computer algorithm optimization, traditional approaches have predominantly focused on recognizing isolated gestures. However, this paradigm proves inadequate when confronted with complex gestural sequences, resulting in cumbersome recognition processes and diminished accuracy. Contemporary human-computer interaction (HCI) applications often necessitate users to perform intricate series of gestures, rather than isolated movements. Consequently, there is a pressing need for systems capable of not only recognizing individual gestures but also accurately segmenting and interpreting sequences of complex gestures to infer user intent and provide natural, intuitive responses.Drawing parallels with natural language processing (NLP), where understanding complex sentences requires word segmentation, structural analysis, and contextual comprehension, the field of HCI faces similar challenges in multi-complex dynamic gesture interaction. The cornerstone of effective gesture-based interaction lies in precise gesture segmentation, recognition, and intention understanding. The crux of the matter is developing methods to accurately delineate individual gestures within a continuous sequence and establish contextual relationships between them to discern the user's overarching intent. To address these challenges and facilitate more natural and user-friendly multi-complex dynamic gesture interaction, this paper introduces a novel recognition model and segmentation algorithm. The proposed framework draws inspiration from word processing techniques in NLP, applying a list model to the multi-complex gesture task machine. This approach decomposes complex gestural sequences into constituent operations, which are further subdivided into consecutive actions corresponding to individual gestures. By recognizing each gesture independently and then synthesizing this information, the system can interpret the entire complex gesture task. The algorithm incorporates the concept of action elements to reduce gesture dimension and employs a probability density distribution-based segmentation and optimization technique to accurately partition gestures within multi-complex tasks. This innovative approach not only enhances recognition accuracy but also significantly reduces computational complexity, as demonstrated by experimental results on a multi-complex gesture task database.The paper is structured as follows: First, it elucidates the algorithm framework for divisible multi-complex dynamic gesture task recognition and the underlying model based on word processing techniques. Subsequently, it provides a detailed exposition of the algorithm's implementation, encompassing feature extraction, gesture classification, segmentation, and optimization methodologies. Finally, the paper presents the experimental design and results, offering empirical validation of the proposed approach's efficacy.This research represents a significant advancement in the field of gesture recognition, particularly in handling complex, multi-gesture sequences. By addressing the limitations of traditional single-gesture recognition systems, this work paves the way for more sophisticated and intuitive human-computer interaction paradigms. The proposed model's ability to accurately segment and interpret complex gesture sequences opens up new possibilities for applications in various domains, from virtual reality interfaces to robotic control systems. The integration of concepts from NLP into gesture recognition underscores the interdisciplinary nature of this research, highlighting the potential for cross-pollination of ideas between different fields of computer science. Furthermore, the emphasis on reducing computational complexity while maintaining high accuracy addresses a crucial concern in real-time interactive systems. In conclusion, this study makes substantial contributions to the field of gesture recognition and HCI, offering a robust framework for handling multi-complex dynamic gesture tasks. The proposed algorithms and models not only advance the state of the art in gesture recognition but also lay the groundwork for more natural and efficient human-computer interaction modalities in future applications.
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Zheng, Rui, Fei Jiang, and Ruimin Shen. "GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/95.

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Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75.2% mAP on real student behavior dataset, and 74.5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.
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Reports on the topic "Gesture recognition module"

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Yang, Jie, and Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada282845.

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