[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123286-en":3,"doc-seo-123286-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123286,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhancing Human Activity Recognition through Machine Learning Models - A Comparative Study","This study investigates Human Activity Recognition (HAR) as a machine learning approach used for health monitoring and human-computer interaction. HAR determines human actions from sensor streams, particularly accelerometers and gyroscopes available on smartphones and wearable devices. The research emphasizes key pipeline elements including model selection, feature extraction, preprocessing, and data collection to classify activities such as standing, lying, sitting, and walking. The work also addresses privacy risks that motivate further research toward safer deployment. A detailed analysis of HAR techniques is presented throughout.","International Journal of Innovative Technology and Interdisciplinary Sciences  \n[https://journals.tultech.eu/index.php/ijitis](https://journals.tultech.eu/index.php/ijitis)  \nISSN: 2613-7305  \nVolume 8, Issue 1  \nDOI: [https://doi.org/10.15157/IJITIS.2025.8.1.258-271](https://doi.org/10.15157/IJITIS.2025.8.1.258-271)  \nReceived: 18.11.2024; Revised: 27.01.2025; Accepted: 03.03.2025  \nEnhancing Human Activity Recognition through Machine Learning Models: A Comparative Study  \nKatragadda Megha Shyam1, Sindhura Surapaneni2, Pulletikurthy Dedeepya3*, N Sampreet Chowdary3, Balamuralikrishna Thati4  \n1 Department of Computer Science & Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India  \n2 Department of Computer Science and Engineering, NRI Institute of Technology, Agiripalli, India  \n3 Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India  \n4 Department of CSE, Dhanekula Institute of Engineering & Technology. Ganguru,Vijayawada, India  \n* [dedeepya@pvpsit.ac.in](dedeepya@pvpsit.ac.in)  \nAbstract  \nThis study explores Human Activity Recognition (HAR), a machine learning technique utilized in health monitoring and human-computer interaction. HAR identifies human actions through sensor data from accelerometers and gyroscopes in smartphones and wearables. Key components of this technique include model selection, feature extraction, preprocessing, and data collection to classify activities such as standing, lying, sitting, and walking. Despite its potential, privacy concerns warrant further research for effective deployment. A comprehensive analysis of HAR techniques has been described in this research work.  \nKeywords: Accelerometer; Human-Computer Interaction; Censor Data; Recognizing Human Activity; Gesture recognition; Pattern Recognition; Real-Time Monitoring  \nINTRODUCTION  \nHuman Activity Recognition (HAR) is an interdisciplinary field that leverages sensor data to automatically detect and classify human activities. The process typically involves several key phases, including data collection, feature extraction, and model selection. Recent advancements in deep learning and sensor fusion have significantly improved HAR performance, enabling more accurate and efficient activity recognition. However, further research is needed to ensure the effective deployment of these systems in realworld applications.  \nDeep learning models, in particular, have gained significant attention due to their ability to capture complex patterns in large datasets. Additionally, the fusion of data from multiple sensor types has proven to enhance HAR system performance. HAR is particularly important in domains such as fitness tracking, smart environments, and  \nInternational Journal of Innovative Technology and Interdisciplinary Sciences  \n[https://doi.org/10.15157/IJITIS.2025.8.1.258-271](https://doi.org/10.15157/IJITIS.2025.8.1.258-271)  \n© 2024 Authors. This is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License CC BY 4.0 ([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0)).  \n 259  Enhancing Human Activity Recognition through Machine Learning Models: A Comparative Study  \nhealthcare, where it can be used to monitor patient well-being, track daily activities, and detect events such as falls.  \nReal-world applications of HAR extend to improving user experiences by enabling subtle environmental adjustments in response to human behaviour. Computer visionbased approaches are also commonly employed in HAR to track activities via visual data. Many mobile applications rely on sensor-based classification models to recognize and interpret human activity in real time.  \nDespite its potential, HAR faces several challenges. These include data variability, where individuals may perform the same activity in different ways like scalability, as the system must be capable of recognizing a vast range of","cbCaiuw01YbK9SLM","https://ap.wps.com/l/cbCaiuw01YbK9SLM","pdf",1098023,1,14,"English","en",105,"# Introduction\n## Challenges and real-world importance\n# Literature Review\n## Smartphone inertial sensors and SVM variants\n## Wearable sensor approaches for rehabilitation\n# Proposed Comparative Analysis","[{\"question\":\"What problem does the document focus on?\",\"answer\":\"The document focuses on Human Activity Recognition (HAR), aiming to detect and classify human actions using machine learning models fed by sensor data.\"},{\"question\":\"How does HAR work in the discussed approach?\",\"answer\":\"HAR typically follows a pipeline of data collection, feature extraction, preprocessing, and model selection, using accelerometer and gyroscope signals from smartphones and wearables.\"},{\"question\":\"Why are privacy concerns highlighted?\",\"answer\":\"The document notes that activity data can reveal sensitive information, creating ethical and privacy risks that must be carefully addressed during system design and deployment.\"}]","Enhancing Human Activity Recognition through Machine Learning Models - A Comparative Study | PDF",1785815758,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-human-activity-recognition-through-machine-learning-models-a-comparative-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-human-activity-recognition-through-machine-learning-models-a-comparative-study/123286/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document focus on?","Question",{"text":75,"@type":76},"The document focuses on Human Activity Recognition (HAR), aiming to detect and classify human actions using machine learning models fed by sensor data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does HAR work in the discussed approach?",{"text":80,"@type":76},"HAR typically follows a pipeline of data collection, feature extraction, preprocessing, and model selection, using accelerometer and gyroscope signals from smartphones and wearables.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are privacy concerns highlighted?",{"text":84,"@type":76},"The document notes that activity data can reveal sensitive information, creating ethical and privacy risks that must be carefully addressed during system design and deployment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]