[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128583-en":3,"doc-seo-128583-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128583,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Recognizing Sitting Activities of Excavator Operators - Using Multi-Sensor Data Fusion with Machine Learning and Deep Learning Algorithms","Recognizing excavator operators’ sitting activities is vital for strengthening health, safety, and productivity while also clarifying how operators behave and interact with construction equipment. Prior work is limited for this specific sitting-activity scenario, especially under real-site constraints. This study proposes a recognition method that fuses multi-sensor data and applies machine learning and deep learning models. A system combining interface pressure sensor arrays and inertial measurement units is deployed on a construction site, achieving strong accuracy for static postures and compound actions.","1 Recognizing Sitting Activities of Excavator Operators Using Multi-Sensor Data  \n2 Fusion with Machine Learning and Deep Learning Algorithms  \n3 Jue Lia, *, Gaotong Chena, Maxwell Fordjour Antwi-Afarib  \n4 a School of Economics and Management, China University of Geosciences, Wuhan, Hubei, China  \n5 b Department of Civil Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham, 6 UK  \n7 * Corresponding author at: School of Economics and Management, China University of Geosciences, Wuhan, Hubei,  \n8 China. E-mail address: [jueli0824@gmail.com](jueli0824@gmail.com)  \n9 Abstract  \n10 Recognizing excavator operators’ sitting activities is crucial for improving their health, safety, and productivity.  \n11 Moreover, it provides essential information for comprehending operators’ behavior patterns and their interaction with  \n12 construction equipment. However, limited research has been conducted on recognizing excavator operators’ sitting  \n13 activities. This paper presents a method for recognizing excavator operators’ sitting activities by leveraging multi- 14 sensor data and employing machine learning and deep learning algorithms. A multi-sensor system integrating  \n15 interface pressure sensor arrays and inertial measurement units was developed to capture excavator operators’ sitting  \n16 activity information at a real construction site. Results suggest that the gated recurrent unit achieved outstanding  \n17 performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions.  \n18 Moreover, several multi-sensor combination schemes were proposed to strike a balance between practicability and  \n19 recognition accuracy. These findings demonstrate the feasibility and potential of the proposed approach for  \n20 recognizing operators’ sitting activities on construction sites.  \n21 Keywords: Excavator operator; Sitting activity recognition; Multi-sensor fusion; Machine learning; Deep learning;  \n22 Interface pressure  \n23 1. Introduction  \n24 Construction equipment operation plays a vital role on construction sites, relying heavily on the expertise and  \n25 efficiency of construction equipment operators. These operators bear the responsibility of operating a wide range of  \n26 machinery, such as excavators, cranes, bulldozers, and forklifts, which are indispensable for ensuring the safe and  \n27 effective completion of construction projects. Throughout the operation of construction equipment, the activities  \n28 performed by operators dominate the operational process and affect various aspects of construction task performance  \n29 [1] . These activities encompass a diverse range of body postures and compound movements carried out by the  \n30 operator while seated, playing a crucial role in their daily tasks [2-5] . Operators often spend prolonged durations  \n31 operating construction equipment in harsh environments and are often subjected to conditions that pose significant  \n32 construction risks [2-5] . Notably, operators’ sitting activities have been demonstrated to possess profound  \n33 implications for construction performance, including operational safety [6,7], workers’ health [2,3,8,9], and  \n34 production efficiency [10] . Moreover, human body activities serve as fundamental elements reflecting human  \n35 behaviors during interactions with the environment [11] . With the growing presence of machinery operations with  \n36 varying levels of automation at modern construction sites [12], continuous monitoring of operators’ sitting activities  \n37 has become increasingly necessary. Such monitoring provides essential information for modeling and analyzing  \n38 operators’ daily operational performance and behavioral patterns, thereby offering insights for understanding human-  \n39 machine interactions context at the construction sites. Therefore, given the strong relationship between sitting  \n40 activities and construction equipment operation performance, it b","cbCaiedJDKll4EC2","https://ap.wps.com/l/cbCaiedJDKll4EC2","pdf",2972771,2,1,47,"English","en",105,"# Abstract\n## Introduction\n## Activity recognition background\n## Proposed multi-sensor learning approach","[{\"question\":\"Why is sitting activity recognition for excavator operators important?\",\"answer\":\"It improves health, safety, and productivity, and provides information to understand operators’ behavior patterns and interaction with construction equipment.\"},{\"question\":\"What sensor system is used to recognize sitting activities in this study?\",\"answer\":\"It integrates interface pressure sensor arrays and inertial measurement units to capture sitting-activity information at a real construction site.\"},{\"question\":\"Which modeling approach shows the best reported performance?\",\"answer\":\"The gated recurrent unit achieves outstanding performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions.\"}]","Recognizing Sitting Activities of Excavator Operators - Using Multi-Sensor Data Fusion with Machine Learning and Deep Learning Algorithms | PDF",1786001924,118,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"recognizing-sitting-activities-of-excavator-operators-using-multi-sensor-data-fusion-with-machine-learning-and-deep-learning-algorithms","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/recognizing-sitting-activities-of-excavator-operators-using-multi-sensor-data-fusion-with-machine-learning-and-deep-learning-algorithms/128583/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is sitting activity recognition for excavator operators important?","Question",{"text":76,"@type":77},"It improves health, safety, and productivity, and provides information to understand operators’ behavior patterns and interaction with construction equipment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What sensor system is used to recognize sitting activities in this study?",{"text":81,"@type":77},"It integrates interface pressure sensor arrays and inertial measurement units to capture sitting-activity information at a real construction site.",{"name":83,"@type":74,"acceptedAnswer":84},"Which modeling approach shows the best reported performance?",{"text":85,"@type":77},"The gated recurrent unit achieves outstanding performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]