Human Gestures Research Articles

Unsupervised Domain Adaptation for RF-Based Gesture Recognition

Human gesture recognition with Radio Frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this paper, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo-labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo-labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo-labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi dataset and millimeter wave (mmWave) radar dataset, respectively.

Application of a 3D Talking Head as Part of Telecommunication AR, VR, MR System: Systematic Review

In today’s digital era, the realms of virtual reality (VR), augmented reality (AR), and mixed reality (MR) collectively referred to as extended reality (XR) are reshaping human–computer interactions. XR technologies are poised to overcome geographical barriers, offering innovative solutions for enhancing emotional and social engagement in telecommunications and remote collaboration. This paper delves into the integration of (AI)-powered 3D talking heads within XR-based telecommunication systems. These avatars replicate human expressions, gestures, and speech, effectively minimizing physical constraints in remote communication. The contributions of this research encompass an extensive examination of audio-driven 3D head generation methods and the establishment of comprehensive evaluation criteria for 3D talking head algorithms within Shared Virtual Environments (SVEs). As XR technology evolves, AI-driven 3D talking heads promise to revolutionize remote collaboration and communication.

A light-responsive poly(urethane-urea) actuator with room temperature self-healing performance

Light-responsive actuators with self-healing properties and super mechanical strength at room temperature are critical for practical applications. However, optimizing both of these properties on a single actuator is extremely challenging. Herein, a novel light-responsive poly(urethane-urea) actuator (PIBS-0.75) with room temperature self-healing performance has been successfully prepared. The PIBS-0.75 light-responsive actuator not only exhibits excellent self-healing properties (heals for 8 h at room temperature and regains 91.16 % of its original tensile strength), but also shows high tensile strength (15.52 MPa) and good fracture strain (over 600 %). Moreover, a bionic light-responsive device with room temperature self-healing properties has been successfully fabricated to imitate human gestures of greeting. By incorporating the room temperature self-healing ability into the light-responsive actuator, this work would pave a new way for the development of durable and sustainable light-responsive actuators.

ANALISIS KETERAMPILAN GERAK SPIKE BOLAVOLI PEMAIN JUNIOR DI SEKOLAH BOLAVOLI PERVOPA PATEMON TAHUN 2022

Movement skills must be possessed by an athlete, with good and correct mastery of techniques are expected to be outstanding players, especially novice and junior players. Biomechanics is the science of human gestures using the principles of mechanics by combining several disciplines. With technology, biomechanics is growing and becoming a reference by athletes and coaches.
 The purpose of the study was to analyze the spike motion skills of junior players in SBV. PERVOPA Patemon is reviewed in terms of biomechanics which includes the prefix phase, repulsion phase, ball impact phase , landing phase, and total time from start to landing so that it can be used as learning to improve spike movement to be more effective and efficient.
 This type of research is descriptive quantitative. The sample is the 2 best SBV athletes. PERVOPA Patemon who has good and correct spike skills with open spike and quick spike positions. The instrument used is in the form of video documentation using a spike skill test from Nurhasan which will be analyzed for movement using kinovea software, as well as its power spike.
 The results of the research Pervopa Patemon's unior athlete spike technique skill level of 100% good category. Open spikes have a total time from the prefix phase to the landing phase which is longer compared to a quick spike. The time of hitting the ball, the height of the jump, and the body mass greatly affect the results of the power spike.

Human Gesture Recognition Based on CT-A Hybrid Deep Learning Model in Wi-Fi Environment

Human Gesture Recognition Based on CT-A Hybrid Deep Learning Model in Wi-Fi Environment

RETRACTED ARTICLE: An Improved Approach for Human Movement Gesture Recognition Based on Grey Wolf Optimization and 3D Reconstruction

Statement of Retraction: We, the Editors and Publisher of International Journal of Human Computer — Interaction, have retracted the following article: Mingyang Han (2023) An Improved Approach for Human Movement Gesture Recognition Based on Grey Wolf Optimization and 3D Reconstruction, International Journal of Human — Computer Interaction, DOI: https://doi.org/10.1080/10447318.2023.2277487 Since publication, significant concerns have been raised about the fact that this article has substantial overlap with the following article: Mustafa Z, Nsour H, Tahir SBud. 2023. Hand gesture recognition via deep data optimization and 3D reconstruction. PeerJ Computer Science 9:e1619 https://doi.org/10.7717/peerj-cs.1619 The author has not responded to our queries about this significant level of overlap. As this is a serious breach of our Editorial Policies, we are retracting the article from the journal. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as &‘Retracted&’.

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HGR faces challenges such as the high similarity of hand gestures, real-time performance, and model generalization. To address these challenges, this paper proposes the stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. An initially pretrained vision transformer (ViT) featuring a self-attention mechanism is introduced to effectively capture intricate connections among image patches, thereby enhancing its capability to handle the challenge of high similarity between hand gestures. Subsequently, knowledge distillation is proposed to compress the ViT model and improve model generalization. Multiple distilled ViTs are then stacked to achieve higher predictive performance and reduce overfitting. The proposed SDViT model achieves a promising performance on three benchmark datasets for hand gesture recognition: the American Sign Language (ASL) dataset, the ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. The accuracies achieved on these datasets are 100.00%, 99.60%, and 100.00%, respectively.

Optimal video processing and soft computing algorithms for human hand gesture recognition from real-time video

Optimal video processing and soft computing algorithms for human hand gesture recognition from real-time video

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review.

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.

Human Body Gesture as Semantic Factor in Visual Education: A Review towards Design Language

Human Body Gesture as Semantic Factor in Visual Education: A Review towards Design Language

Weight thresholded regularized robust coding for vision sensing-based hand gesture detection in collaborative robotics

Vision sensing-based hand gesture recognition is considered an important contemporary research problem of collaborative robotics in a human-robot coexisting environment. The problem becomes more complex in challenging environments, where a robot needs to correctly recognize a human gesture to perform navigation or some other designated job, even in the presence of poor illumination conditions, occlusion, etc. This work proposes a novel approach of successfully utilizing regularized robust coding (RRC)-based models to solve such hand gesture detection problems in real-life, challenging situations. The RRC model is used for robust regression of a signal or image and is known as an improvement over the classical sparse representation-based classification (SRC) model. In this work, we propose three novel variants of weight thresholding mechanism in conjunction with RRC (named WTRRC algorithms) which essentially employ more error-tolerant concepts of logistic functions for weight update, when the dictionary is formed by real-world, photometrically irregular hand gesture images. Extensive case studies in real-world environments (i) with poor illumination, and (ii) with both poor illumination and occlusions firmly establish superior performances of the proposed WTRRC variants compared to other state-of-the-art algorithms in such collaborative robotics problems.

A Comparison of Machine Learning Algorithms for Wi-Fi Sensing Using CSI Data

In today’s digital era, our lives are deeply intertwined with advancements in digital electronics and Radio Frequency (RF) communications. From cell phones to laptops, and from Wireless Fidelity (Wi-Fi) to Radio Frequency IDentification (RFID) technology, we rely on a range of electronic devices for everyday tasks. As technology continues to evolve, it presents innovative ways to harness existing resources more efficiently. One remarkable example of this adaptability is the utilization of Wi-Fi networks for Wi-Fi sensing. With Wi-Fi sensing, we can repurpose existing networking devices not only for connectivity but also for essential functions like motion detection for security systems, human motion tracking, fall detection, personal identification, and gesture recognition using Machine Learning (ML) techniques. Integrating Wi-Fi signals into sensing applications expands their potential across various domains. At the Gamgee, we are actively researching the utilization of Wi-Fi signals for Wi-Fi sensing, aiming to provide our clients with more valuable services alongside connectivity and control. This paper presents an orchestration of baseline experiments, analyzing a variety of machine learning algorithms to identify the most suitable one for Wi-Fi-based motion detection. We use a publicly available Wi-Fi dataset based on Channel State Information (CSI) for benchmarking and conduct a comprehensive comparison of different machine learning techniques in the classification domain. We evaluate nine distinct ML techniques, encompassing both shallow learning (SL) and deep learning (DL) methods, to determine the most effective approach for motion detection using Wi-Fi router CSI data. Our assessment involves six performance metrics to gauge the effectiveness of each machine learning technique.

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.

On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures.

We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure.

ACT2G

Recent increase of remote-work, online meeting and tele-operation task makes people find that gesture for avatars and communication robots is more important than we have thought. It is one of the key factors to achieve smooth and natural communication between humans and AI systems and has been intensively researched. Current gesture generation methods are mostly based on deep neural network using text, audio and other information as the input, however, they generate gestures mainly based on audio, which is called a beat gesture. Although the ratio of the beat gesture is more than 70% of actual human gestures, content based gestures sometimes play an important role to make avatars more realistic and human-like. In this paper, we propose a attention-based contrastive learning for text-to-gesture (ACT2G), where generated gestures represent content of the text by estimating attention weight for each word from the input text. In the method, since text and gesture features calculated by the attention weight are mapped to the same latent space by contrastive learning, once text is given as input, the network outputs a feature vector which can be used to generate gestures related to the content. User study confirmed that the gestures generated by ACT2G were better than existing methods. In addition, it was demonstrated that wide variation of gestures were generated from the same text by changing attention weights by creators.

Review for Optimal Human-Gesture Design Methodology and Motion representation of Medical Images using segmentation from depth data and gesture recognition.

Human gesture recognition and motion representation has become a vital base of current intelligent human-machine interfaces because of ubiquitous and more comfortable interaction. Human-Gesture recognition chiefly deals with recognizing meaningful, expressive body movements involving physical motions of face, head, arms, fingers, hands or body. This review article presents a concise overview of optimal human-gesture and motion representation of medical images. This paper surveys various works undertaken on human gesture design and discusses various design methodologies used for image segmentation and gesture recognition. It further provides a general idea of modeling techniques for analysing hand gesture images and even discusses the diverse techniques involved in motion recognition. This survey provides an insight into various efforts and developments made in the gesture/motion recognition domain through analyzing and reviewing the procedures, datasets, recognition rates and approaches employed for identifying diverse human motions and gestures for supporting better and devising improved applications in near future.

High-Linearity Flexible Pressure Sensor Based on the Gaussian-Curve-Shaped Microstructure for Human Physiological Signal Monitoring.

Flexible pressure sensors with high-performance show broad application prospects in health monitoring, wearable electronic devices, intelligent robot sensing, and other fields. Although flexible pressure sensors have made significant progress in sensitivity and detection range, most of them still exhibit strong nonlinearity, which leads to significant troubles in signal acquisition and thus limits their popularity in practical applications. It remains a serious challenge for the flexible pressure sensor to achieve high linearity while maintaining high sensitivity. Herein, a doped sensing membrane with a uniformly distributed Gaussian-curve-shaped micropattern array was developed using the micro-electromechanical systems (MEMS) process, and a flexible sensor structure with the doped film as the core was designed and constructed. The prototype sensor has a high sensitivity of 1.77 kPa-1 and a linearity of 0.99 in the full detection range of 20 Pa to 30 kPa. In addition, its excellent performance also includes fast response/recovery times (∼25/50 ms) and long-term endurance (>10,000 cycles at 15 kPa). The prototype sensor has been successfully demonstrated in human pulse monitoring, speech recognition, and gesture recognition. The 2 × 6 sensor array can detect the spatial pressure distribution. Thus, such a microstructure shape design will open a new way to fabricate a high-linearity pressure sensor for potential applications in health monitoring, human-machine interaction, etc.

Lightweight Football Motion Recognition and Intensity Analysis Using Low-Cost Wearable Sensors.

In recent years, machine learning has been utilized in health informatics and sports science. There is a great demand and development potential for combining the Internet of Things (IoT) and artificial intelligence (AI) to be applied to football sports. The conventional teaching and training methods of football sports have limited collection and mining of real raw data using wearable devices, and lack human motion capture and gesture recognition based on sports science theories. In this study, a low-cost AI + IoT system framework is designed to recognize football motion and analyze motion intensity. To reduce the communication delay and the computational resource consumption caused by data operations, a multitask learning model is designed to achieve motion recognition and intensity estimation. The model can perform classification and regression tasks in parallel and output the results simultaneously. A feature extraction scheme is designed in the initial data processing, and feature data augmentation is performed to solve the small sample data problem. To evaluate the performance of the designed football motion recognition algorithm, this paper proposes a data extraction experimental scheme to complete the data collection of different motions. Model validation is performed using three publicly available datasets, and the features learning strategies are analyzed. Finally, experiments are conducted on the collected football motion datasets and the experimental results show that the designed multitask model can perform two tasks simultaneously and can achieve high computational efficiency. The multitasking single-layer long short-term memory (LSTM) network with 32 neural units can achieve the accuracy of 0.8372, F1 score of 0.8172, mean average precision (mAP) of 0.7627, and mean absolute error (MAE) of 0.6117, while the multitasking single-layer LSTM network with 64 neural units can achieve the accuracy of 0.8407, F1 score of 0.8132, mAP of 0.7728, and MAE of 0.5966.

Tactual Articulatory Feedback on Gestural Input

The role of the sense of touch in Human–Computer Interaction as a channel for feedback in manipulative processes is investigated through the research presented here. The paper discusses how information and feedback as generated by the computer can be presented haptically, and focusses on the feedback that supports the articulation of human gesture. A range of experiments are described that investigate the use of (redundant) tactual articulatory feedback. The results presented show that a significant improvement of effectiveness only occurs when the task is sufficiently difficult, while in some other cases the added feedback can actually lower the effectiveness. However, this work is not just about effectiveness and efficiency, it also explores how multimodal feedback can enhance the interaction and make it more pleasurable—indeed, the qualitative data from this research show a perceived positive effect for added tactual feedback in the overall experience. The discussion includes suggestions for further research, particularly investigating the effect in free moving gestures, multiple points of contact, and the use of more sophisticated actuators.

Nurse-Patient Communication and Relationship When Wearing Personal Protective Equipment: Nurses' Experience in a COVID-19 Ward.

Little is known about which communication strategies nurses carried out and whether the nurse-patient relationship has been altered due to the mandated use of personal protective equipment during the COVID-19 pandemic. This study describes how nurse-patient communication and relationships took place from the point of view of nurses engaged in caring for patients with COVID-19. A qualitative descriptive study design following COREQ guidelines was conducted. Semi-structured telephone interviews with nurses working in the COVID ward of an Italian university hospital were performed between September 2020 and June 2021. Ten nurses were recruited using convenience sampling. One overarching theme, three main themes, and nine sub-themes were identified. The overarching theme 'The in-out relationship: 'in here and out there' and 'inside me and out of me' included the main themes 'A closed system different from normal', 'Uncovering meaningful human gestures', and 'A deep experience to live''. The relational nature of nursing-where 'me and you' and the context are the main elements-leads nurses to find new ways of interacting and communicating with patients, even in a new situation that has never been experienced. Enhancing human gestures, thinking about new contexts of care, and educating new generations to maintain human-to-human interaction, regardless of the context of care, are the directives to be explored for creating the future of nursing care.