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human Activity Recognition and Prediction: Overview and Research Gaps

Human Activity Recognition and Prediction: Overview and Rese...

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international conference for Convergence of Technology (I2CT)

作者： Diana Nagpal Shikha Gupta Department of Computer Science and Engineering Chandigarh University Ghauraun India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India

With several applications in the disciplines of healthcare, human-computer interaction, assistive learning, and many others, human Actions Recognition, or HAR, is a trending research area. Despite the fact that this field has seen a lot of development over the past ten years, there is still a need to develop strong hybrid machine learning models for human actions recognition that must meet the objectives of the application, must have solid prediction and high recognition rate. A little work has been done in anomaly detection while performing an action. this paper presents a detailed literature survey in this area. Future directions are also presented based on the literature which shows that there is still a need of hybrid optimization technique to enhance the model's functionality.

关键词： Surveys Solid modeling Sensor placement Medical services Machine learning Predictive models Solids

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Educational Metaverse 17th

Educational Metaverse

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17th international conference on Computational Intelligence in Security for Information Systems, CISIS 2024 and the 15th international conference on EUropean Transnational Education, ICEUTE 2024

作者： Martínez-Gutiérrez, Alberto Díez-González, Javier Ferrero-Guillén, Rubén Sánchez-Calleja, Iván Alonso-Diez, Pablo Araújo, Madalena Perez, Hilde Department of Mechanical Computer and Aerospace Engineering Universidad de León León24071 Spain Faculty of Geagrahic and History Universidad de Salamanca Salamanca37071 Spain ALGORITMI Universidade do Minho Escola de Engenharia Campus Azurem Guimaraes Braga4800-058 Portugal

ISBN: (纸本)9783031750151

Education is a process that has been key to social and technological development through the acquisition of ideas, skills, and knowledge. For this reason, innovations in this field are essential to improve and share this knowledge and experience of people. In this context, digitization has been a major breakthrough, providing flexibility and global access never seen before in education. However, one of the limitations of digital educational platforms is the immersive interaction between the students and the teacher, which is key in training and the generation of crucial experiences in the learning process. For this reason, the authors have developed a virtualized environment called metaverse where humans can see each other and share experiences and knowledge in a completely virtual environment. this novel platform has been tested on engineering students who have performed an internship in both the virtual and the real world in order to compare them. the perception of the students has been collected through a survey where very promising results have been obtained to improve the educational metaverse as a space for academic and human growth. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Virtual environments

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TedGram: Twitter Event Detection using Graphbased Methods 8

TedGram: Twitter Event Detection using Graphbased Methods

引用

8th international conference on Web Research, ICWR 2022

作者： Akhgari, Zahra Malekimajd, Marzieh Rahmani, Hossein Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9781665466264

the proliferation of social networks has made researchers turn to the analysis of these networks. Event detection is one of the important topics in the analysis of social networks, especially Twitter. In this paper, we propose an online graph-based approach, called TedGram, for event detection in Twitter using word embedding techniques and graph partitioning algorithms. In the TedGram model, for each incoming tweet, candidate tweets are gathered from preceding tweets using co-occurrence in entities keywords, and correspondingly the similarity between tweets are computed using the Word Mover's Distance (WMD) algorithm and pretrained word2vec model. In this regard, the TTI (Tweet Tweet interaction) graph is computed and updated using an online greedy community detection method based on the Barabási-Albert generative model. Furthermore, we utilize Latent Dirichlet Allocation (LDA) and WMD to combine duplicate communities for detecting and merging duplicate events. Our proposed method is applied to a sample of the Event2012 dataset and is evaluated regarding Precision, Recall, and Fscore. the experimental results show that TedGram performs well against the existing methods. © 2022 IEEE.

关键词： Embeddings

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Classifying Hand Gestures through EMG Data with Machine Learning 10

Classifying Hand Gestures through EMG Data with Machine Lear...

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Agarwal, Shweta Chadha, Raman Bhati, Bhoopesh Singh Chandigarh University Computer Science and Engineering Department Punjab Mohali India Indian Institute of Information Technology Computer Science and Engineering Department Haryana Sonepat India

ISBN: (纸本)9789380544472

this study explores the application of machine learning techniques in the classification of hand gestures for controlling upper limb neuroprosthetics. the non-stationary nature of sEMG in practical applications presents a challenge in maintaining consistent performance. To address this issue, we applied five distinct machine learning methods to classify hand gesture data collected from 8 sensors through a Myo wristband. the Kaggle dataset provided over 2900 samples from 4 classes, resulting in over 2900 samples per group. We evaluated the performance of the classification methods based on various metrics such as precision, recall, F1 score, support, and accuracy. Our findings show that QDA performed the best due to its high accuracy. the study identified and classified four hand gestures based on their performance metrics. Our research provides a simple approach to controlling smart equipment like prosthetic limbs, wheelchairs, entertainment software, and human-machine interaction. the use of machine learning in the classification of hand gestures for neuroprosthetics has the potential to significantly enhance the functionality and ease of use of these devices. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Support vector machines

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Research on A Gesture Recognition Method of Arc Curve

Research on A Gesture Recognition Method of Arc Curve

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international conference on Image, Vision and Computing (ICIVC)

作者： Yiliang Xing Huajun Lei Software Engineering Department Hainan College of Software Technology Qionghai City Hainan Province China

Gesture recognition is an important way of human-computer interaction, improving gesture recognition rate is of great significance for human-computer interaction applications. To solve the problems of few categories and low performance of gesture recognition in traditional methods, a new method of gesture recognition based on circular arc curve is proposed in this paper. Firstly, the hand detection arc curve based on geometric characteristics of the hand is constructed. then, key pixels intersecting with the curve are selected, clustered and are divided into different categories according to their spatial adjacency. then spatial angle of key pixels is calculated by vectorial angle method. Finally, number and angle of the categories are used as gesture features, and gesture recognition is based on decision table. Experimental results show that the method has the characteristics of high recognition rate, high performance and good robustness.

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A Study on an Intelligent Elevator Control interaction System Integrating Palmprint Recognition and Gesture Recognition 4th

A Study on an Intelligent Elevator Control Interaction Syste...

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4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Liu, Jiayu Jia, Wei Zhang, Jing Computer and Information Engineering College HFUT He Fei China

ISBN: (纸本)9789819653171

the imperative pursuit of computer vision technology research lies in developing a more accurate, intelligent, swift, and secure gesture imaging recognition methodology. In this context, we have devised an advanced intelligent elevator control interaction system that seamlessly integrates gesture and palmprint recognition. the system employs the Multi-Feature Robust Alignment Technique (MFRAT) to extract distinctive palmprint features, utilizing point-pair regions for palmprint matching. Gesture recognition is achieved through a sophisticated hybrid modeling approach, incorporating mediapipe and Resnet34. Subsequently, a dynamic frequency localization method is employed to translate and analyze specific elevator gesture commands, effectively executing user instructions and ensuring precise floor navigation. Experimental results underscore the system’s proficiency, with a recognition rate of up to 95.5% for single-frame gesture images and 92.84% for combined gesture images. By harnessing the diversity of gestures and the unique characteristics of palmprints, the system employs non-contact gesture imaging to facilitate seamless, efficient, and secure human-machine interaction between users and elevators, showcasing promising applications across various domains. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Elevator Gesture human-Machine interaction Intelligent Palmprin

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Access Control Mechanism Based on Game theory in the Internet of things Environment 8

Access Control Mechanism Based on Game Theory in the Interne...

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8th IEEE international conference on computer and Communications, ICCC 2022

作者： Zhang, Xinyan The Third College Information Engineering University Zhengzhou China

ISBN: (纸本)9781665450515

In order to solve the problem that the traditional 'centralized' access control technology can no longer guarantee the security of access control in the current Internet of things (IoT)environment, a dynamic access control game mechanism based on trust is proposed. According to the reliability parameters of the recommended information obtained by the two elements of interaction time and the number of interactions, the user's trust value is dynamically calculated, and the user is activated and authorized to the role through the trust level corresponding to the trust value. the trust value and dynamic adjustment factor are introduced into the income function to carry out game analysis to avoid malicious access behavior of users. the hybrid Nash equilibrium strategy of both sides of the transaction realizes the access decision-making work in the IoT environment. Experimental results show that the game mechanism proposed in this paper has a certain restraining effect on malicious nodes and can play a certain incentive role in the legitimate access behavior of IoT users. © 2022 IEEE.

关键词： Access control

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Social Media interaction-Based Mental Health Analysis with a Chat-Bot User Interface 8th

Social Media Interaction-Based Mental Health Analysis with a...

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8th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Kabeer, Aliyah John, Paul Gomez, Serena A. Agarwal, Pooja Ananthanagu, U. Department of Computer Science and Engineering PES University Bangalore India

ISBN: (纸本)9789819976218

In recent times, social media has played a major role in shaping the state of mind of young adults. this means that the content that is interacted with online may subconsciously have a significant effect on one’s mental health. this paper presents a novel approach to detect the mental health state of a user by analyzing their online activity over a period of time and generating a report indicating the same. the user is then allowed to ask any clarifying questions on the generated report, along with general queries on mental health via a chat-bot interface. In order to classify the mental state of the user based on the kind of content posted or interacted with by them, their tweets are scraped and feature vectors of the same are generated. Supervised machine learning algorithms like Support Vector Machines (SVM) and neural network-based models like Long Short-Term Memory (LSTM) are compared for their performance on prediction. A transfer learning approach is also attempted and gives promising results in predicting the classes of the tweets. Natural Language Processing techniques such as question similarity and extractive summarization are utilized in building the chat-bot framework. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chatbot Mental health Question similarity Report Social media Tweets Twitter

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Autonomous Rearrangement Planning Using Object Similarity Learning 12

Autonomous Rearrangement Planning Using Object Similarity Le...

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12th RSI international conference on Robotics and Mechatronics, ICRoM 2024

作者： Jabari, Parmida Babi, Zeynab Ezzati Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798331529734

this study investigates the integration of artificial intelligence in robotics assembly systems, focusing on enhancing flexibility and adaptability in dynamic environments where the robot might not be given the precise objects of the intended construction. To this end, a developed approach is presented which is based on object detection, similarity assessment, and sequence planning for autonomous construction tasks. the system utilizes a YOLOv8 model for object detection and a Siamese network architecture, which facilitates object replacement. It processes two input images, one representing the target structure and another showing the available objects, extracting attributes such as bounding boxes, centers, and orientations. An exact object-matching step is performed before a two-stage similarity assessment. the two-stage similarity assessment prioritizes single-object matches and employs a multi-object similarity network only when necessary. this multi-stage approach is designed to maintain high similarity to the intended design while minimizing possible object shortcomings. During the construction phase, motion planning is performed using a 3-degrees-of-freedom Delta parallel robot, and a 2-finger gripper is utilized for robotics grasping. the results of this study, conducted on wooden bricks of varying shapes and sizes, demonstrate the effectiveness of the YOLOv8 model for object detection, achieving over 80% accuracy across 18 classes with a classification loss of approximately 0.25. Additionally, the Siamese network performed robustly, with a loss of less than 0.01 for both single and multiple object comparisons. © 2024 IEEE.

关键词： Robotic assembly

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A Disease Progression Prediction Model Based on EHR data 24

A Disease Progression Prediction Model Based on EHR data

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Fangcao Yu, Xiaomei Zhang, Jinsong Li, Xue Li, Rui School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (纸本)9798350319934

Electronic Health Records (EHRs) contain patients' diagnosis records, hospitalization records and medication records, in which a large amount of structured time-series data are available. Impressive progresses are achieved in the research of EHR analysis and mortality prediction. However, the existing EHR data are sparse and irregular in nature, which hinder the scientific research and practical applications based on time-series EHR data directly. In this paper, we develop several models based on deep neural networks (DNNs) to explore the mortality prediction of patients. Firstly, an attention mechanism is introduced to extend the Factorization Machine (FM) model which dynamically learns the weights of different feature combinations to gain part of interpretability in the model. Secondly, the Bi-direction Gated Recurrent Unit (BiGRU) is applied to capture the long-term dependencies in the forward and backward directions simultaneously. thirdly, the BiGRU-AFM model is proposed and extensively explored in EHR-based data mining, in which the original features, second-order features and higher-order features are utilized to achieve complete feature combination and comprehensive sentimental interaction in EHRs. Specifically, the Attention-based FM (AFM) part preforms the low-order feature combinations and the BiGRU part captures the higher-order feature interaction vectors. the joint output of them are capable to construct extremely expressive vectors to predict a patients' mortality. Finally, a series of experiments are conducted on public EHR dataset, and the experimental results demonstrate that the proposed BiLSTM-FM model outperforms the state-of-the-art baseline models and it achieves around 97.9% on the widely used metric of Area Under Curve. © 2022 IEEE.

关键词： Forecasting

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