[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122739-en":3,"doc-seo-122739-105":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":4,"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},122739,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Footwear-Integrated Force Sensing Resistor Sensors - A Machine Learning Approach for Categorizing Lower Limb Disorders","Lower limb disorders significantly reduce quality of life and increase disability worldwide, with osteoarthritis affecting the knee, hip, and ankle as major contributors. This study investigates footwear embedded with force-sensing resistor sensors to classify joint-specific disorders during walking. Data from 117 participants were collected using sensor-integrated shoes on a 9-meter predefined walkway, followed by preprocessing and feature extraction to build a structured dataset. Multiple machine learning classifiers were trained and validated with k-fold cross-validation. Results show Random Forest achieving 96% accuracy on the balanced dataset, outperforming other models and supporting accurate, sensor-driven categorization for customized interventions and treatment planning.","Footwear-Integrated Force Sensing Resistor Sensors: A Machine Learning Approach for Categorizing Lower  \nLimb Disorders  \nHafeez Ur Rehman Siddiquia,∗, Sunwan Nawaza , Muhammad Nauman Saeeda , Adil Ali Saleema , Muhammad Amjad Razaa , Ali Razaa , Ahsan  \nAslama , Sandra Dudleyb  \na Institute of Computer Science and Information Technology, Khwaja Fareed University  \nof Engineering and Information Technology, Rahim Yar Khan, 64200, Punjab, Pakistan b Bioengineering Research Centre, School of Engineering, London South Bank  \nUniversity, 103 Borough Road, London, SE1 0AA, United Kingdom  \nAbstract  \nLower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91% . The accuracy scores of the Logistic Regression, Gaus-  \n∗ Correspondance: [Hafeez@kfueit.edu.pk](Hafeez@kfueit.edu.pk)  \nEmail addresses: [Hafeez@kfueit.edu.pk](Hafeez@kfueit.edu.pk) (Hafeez Ur Rehman Siddiqui ),  \n[compoperator.sunwan@kfueit.edu.pk](compoperator.sunwan@kfueit.edu.pk) (Sunwan Nawaz), [naumansaeed0077@gmail.com](naumansaeed0077@gmail.com)  \n(Muhammad Nauman Saeed), [adilalisaleem@gmail.com](adilalisaleem@gmail.com) (Adil Ali Saleem),  \n[Ch.amjadraza@gmail.com](Ch.amjadraza@gmail.com) (Muhammad Amjad Raza), [ali.raza.scholarly@gmail.com](ali.raza.scholarly@gmail.com)  \n(Ali Raza), [ahsan.aslam@kfueit.edu.pk](ahsan.aslam@kfueit.edu.pk) (Ahsan Aslam), [dudleyms@lsbu.ac.uk](dudleyms@lsbu.ac.uk)[ ](dudleyms@lsbu.ac.uk)(Sandra Dudley)  \nPreprint submitted to Engineering Applications of Artificial Intelligence October 10, 2023  \nsian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the models’ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans.  \nKeywords: Lower limb disorder, Hip, Knee, Ankle, Gait Analysis, Force-sensing resistor sensors, Plantar pressure  \n1. Introduction  \nLower limb disorders are a substantial contributor to both disability and lower standards of life around the globe Fatima (2022); Grimmer et al. (2019) .  \nAt present, osteoarthritis (OA) stands as the second most prevalent cause of disability Callahan et al. (2021) . OA has a global impact, affecting approximately 500 million individuals and it is projected that by the year 2030 it will affect one-third of the global population Carr et al. (2012); Hunter et al. (2020) . It is also recognized as one of the prevailing types of arthritis on a global scale, constituting approximately 83% of the overall burden associated with OA Vos et al. (2012) . The prevalent disorders affecting the lower limbs include OA of the knee, hip, and ankle Pirani et al. (2019); Leggit et al. (2022) . Hip and Knee OA are highly prevalent forms of OA on a global scale, affecting approximately 12% of the global population Dell’Isola et al. (2022); Ferreira et al. (2021) . According to Vos et al., the worldwide prevalence of knee osteoarthritis (KO","cbCaiprE3LXaapwE","https://ap.wps.com/l/cbCaiprE3LXaapwE","pdf",4105665,1,30,"English","en",105,"# Introduction\n## Lower limb disorders and osteoarthritis burden\n## Conventional clinical assessment methods\n## Wearable sensing and plantar pressure measurement\n# Methods\n## Footwear-integrated force-sensing resistor sensors\n## Data collection and participant protocol\n## Preprocessing and feature extraction\n## Machine learning classifiers and evaluation\n# Results\n## Balanced dataset performance and comparative accuracy\n## K-fold cross-validation outcomes","[{\"question\":\"What is the goal of the study on lower limb disorders?\",\"answer\":\"To classify lower limb disorders affecting the knee, hip, and ankle using footwear integrated with force-sensing resistor sensors during walking.\"},{\"question\":\"How was the sensor data collected for model training and evaluation?\",\"answer\":\"Data were collected from 117 participants wearing sensor-integrated footwear while walking on a predetermined 9-meter walkway.\"},{\"question\":\"Which machine learning model performed best and what was its accuracy?\",\"answer\":\"The Random Forest model achieved the highest balanced-dataset performance with an accuracy rate of 96%, outperforming the Decision Tree and other models.\"}]","Footwear-Integrated Force Sensing Resistor Sensors - A Machine Learning Approach for Categorizing Lower Limb Disorders | PDF",1785812624,76,{"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},"footwear-integrated-force-sensing-resistor-sensors-a-machine-learning-approach-for-categorizing-lower-limb-disorders","",{"@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/footwear-integrated-force-sensing-resistor-sensors-a-machine-learning-approach-for-categorizing-lower-limb-disorders/122739/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study on lower limb disorders?","Question",{"text":75,"@type":76},"To classify lower limb disorders affecting the knee, hip, and ankle using footwear integrated with force-sensing resistor sensors during walking.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the sensor data collected for model training and evaluation?",{"text":80,"@type":76},"Data were collected from 117 participants wearing sensor-integrated footwear while walking on a predetermined 9-meter walkway.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what was its accuracy?",{"text":84,"@type":76},"The Random Forest model achieved the highest balanced-dataset performance with an accuracy rate of 96%, outperforming the Decision Tree and other models.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]