[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126618-en":3,"doc-seo-126618-105":30,"detail-sidebar-cat-0-en-105":95},{"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},126618,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Towards Prediction of Prosthetics Hand Orientation for Different Grasps Using Machine Learning Algorithm - Research Summary","The study uses machine learning with inertial measurement unit (IMU) data to distinguish two prosthetic hand grasps: cylindrical and hook. A deep artificial neural network (Deep ANN) is proposed to recognize grasp modes by learning the relationship between IMU signals and corresponding hand position. Motion data are collected from an IMU device attached to the prosthetic hand and mapped to hand motions for training. Model performance is evaluated on unseen test data, achieving 100% accuracy for both grasp types and indicating strong potential for future prosthetics control.","University of Tennessee at Chattanooga  \nUTC Scholar  \n\n| UTC Spring Research and Arts Conference | UTC Spring Research and Arts Conference Proceedings 2023 |\n| --- | --- |\n| Towards Prediction of Prosthetics Hand Orientation for Different Grasps Using Machine Learning Algorithm\u003Cbr>Nafiseh Ghaffar Nia\u003Cbr>Erkan Kaplanoglu\u003Cbr>University of Tennessee at Chattanooga\u003Cbr>Follow this and additional works at: [https://scholar.utc.edu/research-dialogues](https://scholar.utc.edu/research-dialogues) |  |\n\nRecommended Citation  \nGhaffar Nia, Nafiseh and Kaplanoglu, Erkan, \"Towards Prediction of Prosthetics Hand Orientation for Different Grasps Using Machine Learning Algorithm\" . ReSEARCH Dialogues Conference proceedings. [https://scholar.utc.edu/research-dialogues/2023/proceedings/1](https://scholar.utc.edu/research-dialogues/2023/proceedings/1) .  \nThis posters is brought to you for free and open access by the Conferences and Events at UTC Scholar. It has been accepted for inclusion in UTC Spring Research and Arts Conference by an authorized administrator of UTC Scholar. For more information, please contact [scholar@utc.edu](scholar@utc.edu).  \nTowards Prediction of Prosthetics Hand Orientation for  \nDifferent Grasps Using Machine Learning Algorithm  \nNafiseh Ghaffar Nia and Dr. Erkan Kaplanoglu  \nComputer Science and Engineering College  \nSummary  \nIn this study, we used the machine learning technique based on IMU data to distinguish two different prosthetic hand grasps named cylindrical and hook.  \nIn order to recognize grasps, we proposed a Deep ANN and achieved a high level of accuracy.  \nMotivation  \nProsthetic hands have become increasingly prevalent in recent years, providing individuals with missing or impaired limbs the ability to perform various daily tasks. However, the lack of proper sensory feedback in these devices can make it difficult for users to control the movement of the prosthetic hand.  \nTo address this issue, researchers have explored using inertial measurement unit (IMU) data to predict the grasping of the prosthetic hand.  \nThe ultimate goal of our work is to develop an optimized system that can accurately and seamlessly convert IMU data into visual prosthetic hand movements.  \nMethods & Materials  \nIn this work, predicting a prosthetic hand’s grasping involves collecting motion data from the IMU device attached to it and mapping data to the corresponding hand position.  \nIMU data in controlling prosthetics hands is an ongoing area of research, with new techniques and approaches being developed to increase the accuracy and effectiveness of these devices. In this project, we proposed a deep neural network, Deep ANN, to predict two hand motions. This model trained on the data to learn the relationship between the IMU data and the hand position in two modes. The accuracy and effectiveness of the model are evaluated through testing on a sample of unseen data.  \nB  \nA  \nFigure1. Prosthetic hand using IMU sensor for Hook (A), Cylindrical (B) motions.  \nDiscussion  \nIMUs can provide information on the orientation and movement of the prosthetic hand, which can be used to estimate its grasping. This approach has shown promise in improving the accuracy and speed of prosthetic hand movements.  \nMachine learning algorithms, such as neural networks, have been employed to learn the relationship between IMU data and hand position, allowing for more accurate predictions in grasping.  \nConclusion  \nUsing machine learning techniques based on IMU data, such as deep neural networks, has shown great results in improving the functionality and control of prosthetic hands. The proposed model achieved 100% accuracy for two hand graspings, hook and cylindrical, demonstrating the potential for this approach tobe used in future developments of prosthetic  \nProject Description & Results  \nIn this work, using IMU data to control a prosthetic hand showed promising results in improving the accuracy and speed of prosthetic hand movements. Our pr","cbCaihOrvBnKm66H","https://ap.wps.com/l/cbCaihOrvBnKm66H","pdf",904868,1,2,"English","en",105,"# Summary\n# Motivation\n# Methods & Materials\n## Dataset and model training\n## IMU-to-hand mapping\n# Discussion\n# Conclusion\n# Project Description & Results\n# References","[{\"question\":\"What grasps does the model distinguish in this study?\",\"answer\":\"The system targets two prosthetic hand grasps: cylindrical and hook. These correspond to two distinct hand motion modes.\"},{\"question\":\"How is IMU data used for grasp prediction?\",\"answer\":\"Motion data are collected from an IMU attached to the prosthetic hand. The IMU signals are mapped to hand position/motion states and used to train the Deep ANN.\"},{\"question\":\"What neural network approach is proposed and how is it evaluated?\",\"answer\":\"A deep artificial neural network (Deep ANN) is proposed to learn the relationship between IMU data and hand motions. Accuracy and loss are evaluated using testing on unseen data.\"},{\"question\":\"What results are reported for prediction accuracy?\",\"answer\":\"The proposed model achieves 100% accuracy for both grasp types (hook and cylindrical). This is presented as evidence of strong potential for future prosthetics development.\"}]","Towards Prediction of Prosthetics Hand Orientation for Different Grasps Using Machine Learning Algorithm - Research Summary | PDF",1785933774,5,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"towards-prediction-of-prosthetics-hand-orientation-for-different-grasps-using-machine-learning-algorithm-research-summary","",{"@graph":36,"@context":89},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/towards-prediction-of-prosthetics-hand-orientation-for-different-grasps-using-machine-learning-algorithm-research-summary/126618/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What grasps does the model distinguish in this study?","Question",{"text":75,"@type":76},"The system targets two prosthetic hand grasps: cylindrical and hook. These correspond to two distinct hand motion modes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is IMU data used for grasp prediction?",{"text":80,"@type":76},"Motion data are collected from an IMU attached to the prosthetic hand. The IMU signals are mapped to hand position/motion states and used to train the Deep ANN.",{"name":82,"@type":73,"acceptedAnswer":83},"What neural network approach is proposed and how is it evaluated?",{"text":84,"@type":76},"A deep artificial neural network (Deep ANN) is proposed to learn the relationship between IMU data and hand motions. Accuracy and loss are evaluated using testing on unseen data.",{"name":86,"@type":73,"acceptedAnswer":87},"What results are reported for prediction accuracy?",{"text":88,"@type":76},"The proposed model achieves 100% accuracy for both grasp types (hook and cylindrical). 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