[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121449-en":3,"doc-seo-121449-105":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},121449,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Sitting Posture Detection and Classification Using Machine Learning Algorithms on RapidMiner","Pressure-sensor cushion pads offer a practical approach for monitoring and classifying sitting posture within ergonomic healthcare designs. This study develops and evaluates a prototype cushion pad with embedded pressure sensors that recognizes multiple postures through machine-learning models. The system integrates hardware sensing with data-processing software, and RapidMiner is used to analyze pressure features. Cross-validation tests evaluate Decision Tree, Naive Bayes, Neural Network, Random Forest, and K-NN, showing accuracy differences with Random Forest achieving the highest reported performance.","Sitting Posture Detection and Classification Using Machine Learning Algorithms on RapidMiner  \nChawakorn Sri-ngernyuang1, Prakrankiat Youngkong2, Jinpitcha Mamom3, Duangruedee Lasuka4  \n1,2Institute of Field Robotics, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand  \n3Faculty of Nursing, Thammasat University, Pathum Thani, Thailand  \n4Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand  \nCorresponding Author: [chawakorn.srin@kmutt.ac.th](chawakorn.srin@kmutt.ac.th)  \nReceived January 5, 2025; Revised March 1, 2025; Accepted May 15, 2025  \nAbstract  \nIntegrating pressure sensors into cushion pads presents a viable posture monitoring and classification solution in innovative healthcare and ergonomic design. In this study, a cushion pad with a pressure sensor implanted that can recognize and classify different postures using machine learning techniques is developed and evaluated. The principal objective is to augment postural awareness and avoid disorders of the muscles. The cushion pad system was created and used by combining software algorithms with hardware sensors. Using a variety of machine learning approaches, RapidMiner, a data science platform, was used to analyze the pressure data to classify postures. The following algorithms are tested using crossvalidation for a robust evaluation: Decision Tree, Naive Bayes, Neural Network, Random Forest, and K-Nearest Neighbors (K-NN) . The outcomes showed that the various algorithms' levels of accuracy varied. The Naive Bayes algorithm demonstrated a lesser accuracy of 55.83% compared to the Decision Tree algorithm's 84.49% accuracy.  \nThe Random Forest algorithm surpassed the others with an accuracy of 85. 98%, while the Neural Network approach produced an accuracy of 82.26% . The k-NN algorithm also yielded promising results, with an accuracy of 82. 01% . According to these results, the Random Forest algorithm outperforms the Decision Tree algorithm for posture categorization in this specific example. A workable approach for enhancing ergonomic health and avoiding posturerelated illnesses is to integrate such machine learning models into a cushion pad with pressure sensor integration that can significantly help proactive posture management.  \nKeywords: Cushion Sensors, Postures Classification, Arduino, Machine Learning  \n1. INTRODUCTION  \nSedentary time means the hours spent at a desk, which can be a decisive factor in comfort and health problems known as office syndrome [1] .  \nThis condition includes symptoms associated with extended sitting at a desk and includes backache, tight neck, and shoulders [2] . These symptoms maybe caused by poor posture and lack of standard arrangements in workplaces, which are common to most firms. Prolonged sitting can lead to disorders of the musculoskeletal system and, therefore, life-long, constant, painful feelings in the lower back, shoulders, and neck [3] . In addition, repetitive strain injuries such as carpal tunnel syndrome and tendonitis, Due to repeated usage or overwork of the the limbs, especially the hands and wrists [4] . Office syndrome, or sedentary disease, is not only a physical ailment but also entails visual strain from spending many hours in front of the computer screen, resulting in conditions like dry and tired-looking eyes, blurred vision, and headaches [5] . Such problems are not only largely attributable to unsuitable lighting conditions or ill-placed displays [6]. Moreover, prolonged sitting may result in inadequate blood circulation, leading to sensations of discomfort, pain, edema in the legs, and an elevated risk of deep vein thrombosis. Another thing that is unfavorable to mental health is the fact that work environments are passive in nature. In the same report, it was established that work environments are likely to cause high levels of stress, anxiety, and depression. To minimize these impacts, one needs to take a break, check the setting is correct, do some exercises, or consult do","cbCailD5c07Hs85a","https://ap.wps.com/l/cbCailD5c07Hs85a","pdf",992394,1,17,"English","en",105,"# Introduction\n## Office syndrome and health impacts\n## Smart cushion motivation and study goal\n# System challenges and considerations\n## Pressure-map acquisition and sensor calibration\n## Adaptive learning for user/environment differences\n## Comfort vs. functionality","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets discomfort and health issues associated with prolonged sedentary desk work, often linked to poor sitting posture and limited ergonomic support.\"},{\"question\":\"How does the proposed system detect and classify posture?\",\"answer\":\"A cushion pad with embedded pressure sensors collects pressure data, which is processed using machine-learning techniques in RapidMiner to classify different sitting postures.\"},{\"question\":\"Which machine-learning algorithm performed best?\",\"answer\":\"Random Forest achieved the highest accuracy (reported at about 85.98%) and outperformed the other tested models in the study’s evaluation.\"}]","Sitting Posture Detection and Classification Using Machine Learning Algorithms on RapidMiner | 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problem does the study address?","Question",{"text":75,"@type":76},"The study targets discomfort and health issues associated with prolonged sedentary desk work, often linked to poor sitting posture and limited ergonomic support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system detect and classify posture?",{"text":80,"@type":76},"A cushion pad with embedded pressure sensors collects pressure data, which is processed using machine-learning techniques in RapidMiner to classify different sitting postures.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning algorithm performed best?",{"text":84,"@type":76},"Random Forest achieved the highest accuracy (reported at about 85.98%) and outperformed the other tested models in the study’s 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