[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127428-en":3,"doc-seo-127428-105":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},127428,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",6,"Technology","IoT-Enabled Assistive Glove for Real-Time Sign Language Translation Using Machine Learning","Real-time gesture translation for American Sign Language is achieved through an IoT-enabled smart glove integrating five flex sensors and an MPU-6050 gyroscope. Sensor signals captured from finger motion and wrist orientation are processed with an Arduino Nano, transmitted via Bluetooth to a mobile application, and classified using a Random Forest model reaching 97% accuracy. Recognized gestures are presented as text and vocalized through a speaker. A learning-oriented view links ASL signs to corresponding vocabulary to support inclusive communication between deaf and hearing users.","IoT-Enabled Assistive Glove for Real-Time Sign Language Translation Using Machine Learning  \nMuhammad Uzair Shahid, Muhammad Mahi Mahessar, Muhammad Salaar, Haris Bin Amir, Muhammad Zabil Mehboob  \nDepartment of Software Engineering, Military College of Signals, National University of Science and Technology, Rawalpindi, Pakistan  \n*[Correspondence](Correspondence:mshahid.bse22mcs@student.nust.edu.pk)[:](Correspondence:mshahid.bse22mcs@student.nust.edu.pk)[mshahid.bse22mcs@student.nust.edu.pk](Correspondence:mshahid.bse22mcs@student.nust.edu.pk),mmahessar.bse22mcs@student.n[ust.edu.](ust.edu.pk)[pk](ust.edu.pk), [msalaar.bse22mcs@student.nust.edu.pk](msalaar.bse22mcs@student.nust.edu.pk), [hamir.bse22mcs@student.nust.edu.pk](hamir.bse22mcs@student.nust.edu.pk), [mmehboob.bse22mcs@student.nust.ed u.pk](mmehboob.bse22mcs@student.nust.ed u.pk)  \nCitation | Shahid. M. U, Mahessar. M. M, Salaar. M, Amir. H. B, Mehboob. M. Z,“IoTEnabled Assistive Glove for Real-Time Sign Language Translation Using Machine Learning”, IJIST, Vol. 07 Issue. 03 pp 1568-1583, July 2025  \nDOI| [https://doi.org/10.33411/ijist/20257315681583](https://doi.org/10.33411/ijist/20257315681583)  \nReceived| June 10, 2025 Revised|July 20, 2025 Accepted|July 22, 2025 Published|July 23, 2025.   \nThis paper presents a real-time system for translating gestures from American Sign  \nLanguage (ASL) using an IoT-enabled smart glove. The glove is equipped with five flex  \nsensors and an MPU-6050 gyroscope to capture finger movements and wrist orientation, processed by an Arduino Nano. Sensor data is transmitted via a Bluetooth module to a mobile application, where a Random Forest machine learning model with 97% accuracy classifies the gestures. The recognized gestures are displayed as text and vocalized through a speaker. Moreover, the app has a feature that allows users to see ASL signs with their corresponding vocabulary, thus enabling accessibility and making language more accessible to learn. It enhances the communication between the deaf and the hearing community since it offers an accurate, portable, and interactive sign recognition application.  \nKeywords: Sign Language (SL), American Sign Language (ASL), Machine Learning (ML)  \nIntroduction:  \nThe deaf and speech-impaired community represents a substantial segment of the global population, with an estimated 430 million people, about 5% of the world's population, experiencing some form of disabling hearing loss [1] . In Pakistan alone, there are approximately 244,196 individuals who are either deaf or have speech impairments [2] . Such individuals, like other members of the community, possess unique abilities and talents that contribute immensely to social value. The lack of adequate means of communication has been a barrier, restricting them from society and its opportunities.  \nSign language plays a vital role in the lives of deaf and mute individuals, serving as their primary mode of communication. It enables them to convey their thoughts, emotions, and needs through gestures and expressions, fostering independence and interaction [3][4] . However, the lack of knowledge of sign language among the general population exacerbates communication challenges, creating a gap that hinders inclusivity and mutual understanding.  \nIn the last few years, IoT has made tremendous progress and has created new opportunities to bridge this communication gap [5] . Solutions derived from IoT are equipping individuals with disabilities by providing intelligent devices and supportive technologies that improve their overall quality of life. IoT technologies have opened new avenues for the deaf and speech-impaired community by enabling real-time translation, smart wearables, and mobile apps that support smooth and accessible communication [6] .  \nOne of the innovations that gained significant popularity for translating sign language into text or speech is sign language gloves. These gloves have gone through significant evolution from sens","cbCaitJnZc1kXatL","https://ap.wps.com/l/cbCaitJnZc1kXatL","pdf",985490,2,1,16,"English","en",105,"# Introduction\n## Related Work\n## System Design and Components","[{\"question\":\"How does the IoT-enabled glove recognize sign language gestures?\",\"answer\":\"The glove uses five flex sensors to measure finger movements and an MPU-6050 gyroscope to capture wrist orientation. An Arduino Nano processes the sensor data and sends it to a mobile app via Bluetooth.\"},{\"question\":\"Which machine learning method is used for gesture classification, and what accuracy is reported?\",\"answer\":\"A Random Forest model classifies the gestures in the mobile application, achieving 97% accuracy as reported in the paper.\"},{\"question\":\"What outputs does the system provide for the recognized gestures?\",\"answer\":\"The app displays the recognized gestures as text and vocalizes them through a speaker, and it also includes a feature that shows ASL signs with their corresponding vocabulary for learning.\"}]","IoT-Enabled Assistive Glove for Real-Time Sign Language Translation Using Machine Learning | PDF",1785938820,40,{"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},"iot-enabled-assistive-glove-for-real-time-sign-language-translation-using-machine-learning","",{"@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/technology/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/iot-enabled-assistive-glove-for-real-time-sign-language-translation-using-machine-learning/127428/",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","2026-08-27","2026-08-05",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},"How does the IoT-enabled glove recognize sign language gestures?","Question",{"text":76,"@type":77},"The glove uses five flex sensors to measure finger movements and an MPU-6050 gyroscope to capture wrist orientation. An Arduino Nano processes the sensor data and sends it to a mobile app via Bluetooth.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning method is used for gesture classification, and what accuracy is reported?",{"text":81,"@type":77},"A Random Forest model classifies the gestures in the mobile application, achieving 97% accuracy as reported in the paper.",{"name":83,"@type":74,"acceptedAnswer":84},"What outputs does the system provide for the recognized gestures?",{"text":85,"@type":77},"The app displays the recognized gestures as text and vocalizes them through a speaker, and it also includes a feature that shows ASL signs with their corresponding vocabulary for learning.","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},[94,98,102,106,111,114,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":30,"slug":117},7,"Healthcare","healthcare",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]