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Using artificial intelligence to predict the next deceptive movement based on video sequence analysis: A case study on a professional cricket player's movements

Journal of Engineering Research (Kuwait)

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Journal of Engineering research (Kuwait) 2025年

作者： Mutawa, A.M. Rajesh Kumar, Korupalli V. K, Hemachandran Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis Perlis 02600 Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70 % accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports. © 2025 The Authors

关键词： Artificial intelligence Cricket Deceptive movements Machine learning Performance optimization

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Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data

Decoding the Obfuscated: Advanced NER Techniques on Commerci...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Alejandro Rodriguez Perez Pablo Rivas Javier Turek Korn Sooksatra Ernesto Quevedo Gisela Bichler Tomas Cerny Laurie Giddens Stacie Petter Dept. of Computer Science School of Eng. & Computer Science Baylor University Human-Robot Collaboration Lab Intelligent Systems Research Intel Labs School of Criminology & Criminal Justice California State University San Bernardino Dept. of Systems and Industrial Eng. College of Engineering The University of Arizona Information Tech. & Decisions Sci. Dept. G. Brint Ryan College of Business University of North Texas School of Business Wake Forest University

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Detecting potential human trafficking activity within online Commercial Sex Advertisements (CSAs) presents unique challenges for Named Entity Recognition (NER) due to the complex and often obfuscated textual content. This paper thoroughly evaluates state-of-the-art language models and tokenization techniques, focusing on their efficacy in the NER task within the context of CSAs. Our results indicate that the Longformer model, equipped with byte-level BPE tokenization, outperforms other models regarding precision, recall, and the $F$ 1 score. The study also uncovers specific areas for improvement, offering avenues for future research. Our findings have significant implications for the automated analysis and monitoring of CSAs for suspected human trafficking activity, which contributes to developing more robust and transparent online ecosystems.

关键词： Adaptation models Biological system modeling Focusing Named entity recognition Tokenization Data models Web sites

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HCIlab at Memotion 2.0 2022: Analysis of Sentiment, Emotion and Intensity of Emotion Classes from Meme Images using Single and Multi Modalities 1

HCILab at Memotion 2.0 2022: Analysis of Sentiment, Emotion ...

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1st Workshop on Multimodal Fact-Checking and Hate Speech Detection, DE-FACTIFY 2022

作者： Nguyen, Thanh Tin Pham, Nhat Truong Nguyen, Ngoc Duy Nguyen, Hai Nguyen, Long H. Kim, Yong-Guk Human Computer Interaction Lab Department of Computer Engineering Sejong University Seoul Korea Republic of Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia Khoury College of Computer Sciences Northeastern University Boston United States Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

Nowadays, memes found on internet are overwhelming. Although they are innocuous and sometimes entertaining, there exist memes that contain sarcasm, offensive, or motivational feelings. In this study, several approaches are proposed to solve the multiple modality problem in analysing the given meme dataset. The imbalance issue has been addressed by using a new Auto Augmentation method and the uncorrelation issue has been mitigated by adopting deep Canonical Correlation Analysis to find the most correlated projections of visual and textual feature embedding. In addition, both stacked attention and multi-hop attention network are employed to efficiently generate aggregated features. As a result, our team, i.e. HCIlab, achieved a weighted F1 score of 0.4995 for sentiment analysis, 0.7414 for emotion classification, and 0.5301 for scale/intensity of emotion classes on the leaderboard. This results are obtained by using concatenation between image and text model and our code can be found at https://***/JMRa8. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Sentiment analysis

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Importance of Wearable Health Monitoring systems Using IoMT;Requirements, Advantages, Disadvantages and Challenges

Importance of Wearable Health Monitoring Systems Using IoMT;...

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International Symposium on Computational Intelligence and Informatics

作者： Fahime Khozeimeh Mohamad Roshanzamir Afshin Shoeibi Mohammad Tayarani Darbandy Roohallah Alizadehsani Hamid Alinejad-Rokny Davood Ahmadian Abbas Khosravi Saeid Nahavandi Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Department of Computer Engineering Fasa University Fasa Iran Internship in Health Data Analytics Program AI-enabled Processes (AIP) Research Centre Macquarie University Sydney Australia School of Architecture Islamic Azad University Taft Taft Iran BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia Faculty of Mathematics Statistics and Computer Sciences University of Tabriz Tabriz Iran

ISBN: (纸本)9798350398830

Providing better quality of service is one of the most important goals in medical/healthcare systems. The services provided should have features such as low latency, appropriate geographic distribution, and real-time interaction. Internet of Medical Things (IoMT) technology is one of the solutions to achieve high quality of services with these features. The wearable health monitoring systems which have become popular in the last decade are one of the foundations of achievement to these features. In this paper, the importance of wearable devices under IoMT technology is investigated. There are several reasons for using wearable devices under IoMT technology, such as reducing healthcare costs and long waiting times. Therefore, anyone working on wearable devices needs to have a general understanding of how they are designed and function. In fact, the integration of new devices to monitor and alert patients about their physical health is a promising option for improving health care systems around the world. Overcrowding in medical centers will be prevented by upgrading the facilities for the virtual presence of patients. It can also ensure real-time monitoring and reduce operating costs. In addition, it can accelerate treatment of patients and reduce the cost of investment and increase its coverage.

关键词： Costs Medical devices Wearable computers Quality of service Organizations Real-time systems Security

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The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

arXiv

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arXiv 2024年

作者： Haag, David Kumar, Devender Gruber, Sebastian Hofer, Dominik Sareban, Mahdi Treff, Gunnar Niebauer, Josef Bull, Christopher Schmidt, Albrecht Smeddinck, Jan David Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Department of Psychology Paris-Lodron-University of Salzburg Salzburg Austria Digital Health Information Systems Center for Health & Bioresources AIT Austrian Institute of Technology GmbH Graz Austria University of Southern Denmark Odense Denmark Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria Human Motion Analytics Salzburg Research Forschungsgesellschaft Salzburg Austria LMU Munich Munich Germany University Institute for Sports Medicine Prevention and Rehabilitation Salzburg Austria Institute of Molecular Sports and Rehabilitation Medicine Paracelsus Medical University Salzburg Austria Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing. © 2024, CC BY.

关键词： Electronic health record

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A Unified Perception Benchmark for Capacitive Proximity Sensing Towards Safe human-Robot Collaboration (HRC)

A Unified Perception Benchmark for Capacitive Proximity Sens...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Serkan Ergun Yitao Ding Hosam Alagi Christian Schöffmann Barnaba Ubezio Gergely Soti Michael Rathmair Stephan Mühlbacher-Karrer Ulrike Thomas Björn Hein Michael Hofbaur Hubert Zangl Institute of Smart Systems Technologies Klagenfurt University (AAU) Klagenfurt am Wörthersee Austria Lab of Robotics and Human-Machine-Interaction Chemnitz University of Technology (TUC) Chemnitz Germany Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics Lab (IAR - IPR) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Joanneum Research Robotics (Jr) Institute of Robotics and Mechatronics Klagenfurt am Wörthersee Austria Karlsruhe University of Applied Sciences

During the co-presence of human workers and robots, measures are required to avoid injuries from undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with fast close-range perception for HRC tasks, closing the perception gap between tactile detection and mid-range perception. CPSs do not suffer from occlusion and compared to pure tactile or force sensing, they react earlier and allow increasing the operating speed of Collaborative Robots (Cobots) while still maintaining safety. However, since capacitive coupling to obstacles varies with their distance, shape and material properties, the projection from capacitance to actual distances is a general problem. In this work, we propose an universal benchmark test procedure for fellow researchers to evaluate their CPSs. Considering ISO/TS 15066 for Power and Force Limiting (PFL) as a reference, we derive the requirements for the specified body regions and propose a method for determining the operation speed to comply with PFL based on a pre-defined detection threshold. Finally, the benchmark test procedure is evaluated on three different concepts of CPSs from the contributed researchers, demonstrating the general applicability.

关键词： Shape Force Collaboration Benchmark testing Robot sensing systems Manipulators Sensors

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Guidelines for the development of immersive virtual reality software for cognitive neuroscience and neuropsychology: The development of virtual reality everyday assessment lab (VR-EAL), a neuropsychological test battery in immersive virtual reality.

TechRxiv

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TechRxiv 2021年

作者： Kourtesis, Panagiotis Korre, Danai Collina, Simona Doumas, Leonidas A.A. MacPherson, Sarah E. Human Cognitive Neuroscience Department of Psychology University of Edinburgh Edinburgh United Kingdom Department of Psychology University of Edinburgh Edinburgh United Kingdom Lab of Experimental Psychology Suor Orsola Benincasa University of Naples Naples Italy Interdepartmental Centre for Planning and Research Scienza Nuova Suor Orsola Benincasa University of Naples Naples Italy Centre for Intelligent Systems and their Applications School of Informatics University of Edinburgh Edinburgh United Kingdom

Virtual reality (VR) head-mounted displays (HMD) appear to be effective research tools, which may address the problem of ecological validity in neuropsychological testing. However, their widespread implementation is hindered by VR induced symptoms and effects (VRISE) and the lack of skills in VR software development. This study offers guidelines for the development of VR software in cognitive neuroscience and neuropsychology, by describing and discussing the stages of the development of Virtual Reality Everyday Assessment lab (VR-EAL), the first neuropsychological battery in immersive VR. Techniques for evaluating cognitive functions within a realistic storyline are discussed. The utility of various assets in Unity, software development kits, and other software are described so that cognitive scientists can overcome challenges pertinent to VRISE and the quality of the VR software. In addition, this pilot study attempts to evaluate VR-EAL in accordance with the necessary criteria for VR software for research purposes. The VR neuroscience questionnaire (VRNQ;Kourtesis et al., 2019b) was implemented to appraise the quality of the three versions of VR-EAL in terms of user experience, game mechanics, in-game assistance, and VRISE. Twenty-five participants aged between 20 and 45 years with 12-16 years of full-time education evaluated various versions of VR-EAL. The final version of VR-EAL achieved high scores in every sub-score of the VRNQ and exceeded its parsimonious cut-offs. It also appeared to have better in-game assistance and game mechanics, while its improved graphics substantially increased the quality of the user experience and almost eradicated VRISE. The results substantially support the feasibility of the development of effective VR research and clinical software without the presence of VRISE during a 60-minute VR session. © 2021, CC BY.

关键词： Virtual reality

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Guidelines for the development of immersive virtual reality software for cognitive neuroscience and neuropsychology: The development of Virtual Reality Everyday Assessment lab (VR-EAL), a neuropsychological test battery in immersive virtual reality

arXiv

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arXiv 2021年

作者： Kourtesis, P.K. Panagiotis Korre, Danai Collina, Simona Doumas, Leonidas A.A. MacPherson, Sarah E. Human Cognitive Neuroscience Department of Psychology University of Edinburgh Edinburgh United Kingdom Department of Psychology University of Edinburgh Edinburgh United Kingdom Lab of Experimental Psychology Suor Orsola Benincasa University of Naples Naples Italy Interdepartmental Centre for Planning and Research "Scienza Nuova" Suor Orsola Benincasa University of Naples Naples Italy Centre for Intelligent Systems and their Applications School of Informatics University of Edinburgh Edinburgh United Kingdom

关键词： Virtual reality

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vieCap4H-VLSP 2021: Vietnamese Image Captioning for Healthcare Domain using Swin Transformer and Attention-based LSTM

arXiv

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arXiv 2022年

作者： Nguyen, Thanh Tin Nguyen, Long H. Pham, Nhat Truong Nguyen, Liu Tai Do, Van Huong Nguyen, Hai Nguyen, Ngoc Duy Human Computer Interaction Lab Sejong University Seoul Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam ASICLAND Suwon Korea Republic of Khoury College of Computer Sciences Northeastern University Boston United States Institute for Intelligent Systems Research and Innovation Deakin University Victoria Australia

This study presents our approach on the automatic Vietnamese image captioning for healthcare domain in text processing tasks of Vietnamese Language and Speech Processing (VLSP) Challenge 2021, as shown in Figure 1. In recent years, image captioning often employs a convolutional neural network-based architecture as an encoder and a long short-term memory (LSTM) as a decoder to generate sentences. These models perform remarkably well in different datasets. Our proposed model also has an encoder and a decoder, but we instead use a Swin Transformer in the encoder, and a LSTM combined with an attention module in the decoder. The study presents our training experiments and techniques used during the competition. Our model achieves a BLEU4 score of 0.293 on the vietCap4H dataset, and the score is ranked the 3rd place on the private leaderboard. Our code can be found at https://***/JDdJm. © 2022, CC BY.

关键词： Long short-term memory

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Corrigendum to "First few seconds for flow: A comprehensive proposal of the neurobiology and neurodynamics of state onset" [Neurosci. Biobehav. Rev. 143 (2022)]

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Neuroscience and biobehavioral reviews 2023年 147卷 105086页

作者： Steven Kotler Michael Mannino Scott Kelso Richard Huskey Flow Research Collective United States. Human Brain & Behavior Laboratory Center for Complex Systems and Brain Sciences Florida Atlantic University United States Intelligent Systems Research Centre Ulster University Derry∼Londonderry North Ireland. Cognitive Communication Science Lab Department of Communication University of California Davis United States Cognitive Science Program University of California Davis United States Center for Mind and Brain University of California Davis United States. Electronic address: rwhuskey@ucdavis.edu.

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