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One of the most challenging issues for computer vision is the automatic and precise identification of human" /> <meta name="twitter:image" content="https://0.academia-photos.com/128920438/35786042/30901429/s200_tangina.sultana.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities" /> <meta property="og:title" content="Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. 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One of the most challenging issues for computer vision is the automatic and precise identification of human" /> <title>(PDF) Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities | Tangina Sultana - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '9387f500ddcbb8d05c67bef28a2fe0334f1aafb8'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733046857000); window.Aedu.timeDifference = new Date().getTime() - 1733046857000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...","author":[{"@context":"https://schema.org","@type":"Person","name":"Tangina Sultana"}],"contributor":[],"dateCreated":"2023-07-17","dateModified":"2023-07-17","datePublished":null,"headline":"Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities","inLanguage":"en","keywords":["Computer Science","Artificial Intelligence","Human Computer Interaction","Analytical Chemistry","Sensors","Action Recognition","Action (Physics)","Electrical And Electronic Engineering"],"locationCreated":null,"publication":"Sensors","publisher":{"@context":"https://schema.org","@type":"Organization","name":"MDPI 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One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...","publisher":"MDPI AG","publication_name":"Sensors"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [128920438]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":104332123,"attachmentType":"pdf"}"><img alt="First page of “Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/104332123/mini_magick20230717-1-anxq4p.png?1689616689" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="128920438" href="https://khu.academia.edu/TanginaSultana"><img alt="Profile image of Tangina Sultana" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/128920438/35786042/30901429/s65_tangina.sultana.jpg" />Tangina Sultana</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">Sensors</p></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and ...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":104332123,"attachmentType":"pdf","workUrl":"https://www.academia.edu/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":104332123,"attachmentType":"pdf","workUrl":"https://www.academia.edu/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="104332123" data-landing_url="https://www.academia.edu/104663980/Human_Action_Recognition_A_Taxonomy_Based_Survey_Updates_and_Opportunities" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="88045110" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88045110/Human_Action_Recognition_Using_Deep_Learning">Human Action Recognition Using Deep Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">The goals of video analysis tasks have changed significantly over time, shifting from inferring the current state to forecasting the future state. Recent advancements in the fields of computer vision and machine learning have made it possible. Different human activities are inferred in tasks based on vision-based action recognition based on the full motions of those acts. By extrapolating from that person's current actions, it also aids in the prognosis of that person's future action. Since it directly addresses issues in the real world, such as visual surveillance, autonomous cars, entertainment, etc., it has been a prominent topic in recent years. To create an effective human action recognizer, a lot of study has been done in this area. Additionally, it is anticipated that more work will need to be done. In this sense, human action recognition has a wide range of uses, including patient monitoring, video surveillance, and many more. Two CNN and LRCN models are put out in this article. The findings show that the recommended approach performs at least 8% more accurately than the traditional two-stream CNN method. 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class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="89291374" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89291374/A_Close_Look_into_Human_Activity_Recognition_Models_using_Deep_Learning">A Close Look into Human Activity Recognition Models using Deep Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="101461231" href="https://independent.academia.edu/NaeemSeliya">Naeem Seliya</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Close Look into Human Activity Recognition Models 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The aim of HARis to automatically identify and analyse human activities using acquired information from video data. Some of the applications include security and surveillance, smart homes and assisted living, health monitoring, robotics, human– computer interaction, intelligent driving, video-retrieval, gaming and entertainment etc. This paper explores the impact of Deep Learning techniques on action recognition. We also explore how spatiotemporal features are aggregated through various deep architectures, the role of optical flow as an input, the impacts on real-time capabilities, and the compactness & interpretability of the learned features. Although several papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A review on Human Action Recognition in videos using Deep Learning","attachmentId":81426314,"attachmentType":"pdf","work_url":"https://www.academia.edu/72536482/A_review_on_Human_Action_Recognition_in_videos_using_Deep_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/72536482/A_review_on_Human_Action_Recognition_in_videos_using_Deep_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="53352795" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/53352795/An_Advanced_Approach_to_Recognize_Human_Activities_via_Deep_Learning">An Advanced Approach to Recognize Human Activities via Deep Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="196365830" href="https://mnnit.academia.edu/AKarn">Aryan Karn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Applied Sciences and Technology</p><p class="ds-related-work--abstract ds2-5-body-sm">The study of wearable and handheld sensors recognizing human activity improved our understanding of human behaviours and human objectives. Many academics seek to identify the activities of a user from raw data using the fewest necessary resources. In this article, we propose a network of profound beliefs, a full-service architecture for the recognition of activities (DBN-LSTM). This DBN-LSTM method improves the human predictability of raw data and reduces the complexity of the model as well as the requirement for comprehensive workmanship. A geographically and temporally rich network is CNN-LSTM. Our proposed model for the UCI HAR Public Data Set can achieve 99% accuracy and 92% precision.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Advanced Approach to Recognize Human Activities via Deep Learning","attachmentId":70237454,"attachmentType":"pdf","work_url":"https://www.academia.edu/53352795/An_Advanced_Approach_to_Recognize_Human_Activities_via_Deep_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/53352795/An_Advanced_Approach_to_Recognize_Human_Activities_via_Deep_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="121177720" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/121177720/Real_Time_Human_Activity_Recognition_Using_Deep_Learning">Real Time Human Activity Recognition Using Deep Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2328357" href="https://independent.academia.edu/JournalofComputerScienceIJCSIS">Journal of Computer Science IJCSIS</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">With the increasing number of anti-social products, security is now given more importance. Many organizations have installed CCTV to monitor people and their interactions at all times. For a developed country of 64 million people, each person is caught on camera 30 times a day. A large amount of video data was generated and stored for a specific period of time. A 704x576 image recorded at 25fps will generate about 20GB per day. Constantly monitoring data to judge whether events are abnormal is a nearly impossible task because it requires constant management and attention. This makes it necessary to automate the same. Additionally, it is important to identify which frames and which fractions contain abnormal activity which helps to quickly decide if the abnormal activity is abnormal This is done by rotating video frames and the individuals and their activity types analyzed from the processed frame. Machine learning and deep learning algorithms and methods help us in widespread adoption to make this possible.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Real Time Human Activity Recognition Using Deep Learning","attachmentId":116127342,"attachmentType":"pdf","work_url":"https://www.academia.edu/121177720/Real_Time_Human_Activity_Recognition_Using_Deep_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/121177720/Real_Time_Human_Activity_Recognition_Using_Deep_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="88881215" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88881215/Human_Activity_Recognition">Human Activity Recognition</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Due to the extensive use of various sensors, human activity detection has recently gained popularity in a variety of domains such as person monitoring and human-robot interaction. The main goal of the proposed system is to create an activity detection model that aims to identify human actions through video using deep learning. The dataset called Kinetics is utilised to train the activity recognition model. Convolutional Neural Network (CNN) is one of these techniques. It is a type of neural network in deep learning that has the ability to turn on the underdone inputs immediately. These models can only currently handle inputs that are two dimensional. However, in order to detect the actions in the videos, this study uses a three-dimension CNN model for classification of videos. Since 3D convolutional networks naturally apply convolutions in 3D space, they are recommended for video categorization.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Human Activity Recognition","attachmentId":92782565,"attachmentType":"pdf","work_url":"https://www.academia.edu/88881215/Human_Activity_Recognition","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88881215/Human_Activity_Recognition"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="114631714" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/114631714/Enhanced_Recognition_of_Human_Activity_using_Hybrid_Deep_Learning_Techniques">Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="293247179" href="https://independent.academia.edu/AbinayaS137">Abinaya S</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="303709566" href="https://independent.academia.edu/PottiSaiPavanGuruJayanth">Potti Sai Pavan Guru Jayanth</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64876522" href="https://independent.academia.edu/ForexJournal">FOREX Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">FOREX Publication, 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">In the domain of deep learning, Human Activity Recognition (HAR) models stand out, surpassing conventional methods. These cutting-edge models excel in autonomously extracting vital data features and managing complex sensor data. However, the evolving nature of HAR demands costly and frequent retraining due to subjects, sensors, and sampling rate variations. To address this challenge, we introduce Cross-Domain Activities Analysis (CDAA) combined with a clustering-based Gated Recurrent Unit (GRU) model. CDAA reimagines motion clusters, merging origin and destination movements while quantifying domain disparities. Expanding our horizons, we incorporate image datasets, leveraging Convolutional Neural Networks (CNNs). The innovative aspects of the proposed hybrid GRU_CNN model, showcasing its superiority in addressing specific challenges in human activity recognition, such as subject and sensor variations. This approach consistently achieves 98.5% accuracy across image, UCI-HAR, and PAMAP2 datasets. It excels in distinguishing activities with similar postures. Our research not only pushes boundaries but also reshapes the landscape of HAR, opening doors to innovative applications in healthcare, fitness tracking, and beyond.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques","attachmentId":111278533,"attachmentType":"pdf","work_url":"https://www.academia.edu/114631714/Enhanced_Recognition_of_Human_Activity_using_Hybrid_Deep_Learning_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/114631714/Enhanced_Recognition_of_Human_Activity_using_Hybrid_Deep_Learning_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":104332123,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":104332123,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_104332123" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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