[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127626-en":3,"doc-seo-127626-105":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},127626,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion - Preliminary Study for Smart Prosthetic Hand","EMG signals exhibit random, non-linear, and non-stationary behavior, requiring careful feature extraction and classifier selection for effective prosthetic hand control. This study implements EMG pattern recognition on an embedded Raspberry Pi system as a preliminary step toward a smart prosthetic hand. Time-domain feature extraction (MAV, RMS, VAR) and supervised classifiers (DT, SVM, KNN) are integrated for online training and real-time motor control using Python. Experiments with ten healthy respondents use flexor carpi radialis and extensor digitorum EMG leads. Results achieve 97.03% accuracy for the open class, with significant dataset-related improvement (p \u003C 0.05), supporting embedded deployment for EMG-based prostheses.","Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand  \nTriwiyanto1, Sari Luthfiyah1, Wahyu Caesarendra2, Abdussalam Ali Ahmed3  \n1 Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Indonesia  \n2 Faculty of Integrated Technologies, Universiti Brunei Darussalam, Brunei Darussalam  \n3 Department of Mechanical and Industrial Engineering, Bani Waleed University, Libya  \n\n| Article history:\u003Cbr>Received Dec 30, 2022 Revised Jun 11, 2023 Accepted Aug 6, 2023 | EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03% . Furthermore, the additional datasets show a significant difference in accuracy (p-value \u003C0.05) . Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition.\u003Cbr>Copyright © 2023 Institute of Advanced Engineering and Science.\u003Cbr>All rights reserved |\n| --- | --- |\n| Keywords:\u003Cbr>Surface EMG, Pattern recognition, Hand motion, Prosthetic hand, Embedded system, Machine learning |  |\n\nCorresponding Author:  \nTriwiyanto,  \nDepartment of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Indonesia,  \nJl. Pucang Jajar Timur No. 10, Surabaya, Indonesia.  \nEmail: [triwiyanto@ieee.org](triwiyanto@ieee.org)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nSurface electromyography signal (EMG) is a bioelectric signal generated by muscles during contraction [1][2] . EMG signals are widely used to develop rehabilitation equipment, including prosthetic hands [3][4] . Recently, the development of prosthetic hand has progressed rapidly in terms of controller technology [5], sensor system [6], power management [7], and 3D printing design [8][9] . An EMG-based smart prosthetic hand is expected to recognize EMG signal patterns for different movements and adapt to new users. However, EMG signals have a random and stochastic nature [10] that tends to change every time, even with the same contraction. Furthermore, EMG signals are non-linear and non-stationary [11]–[14] with respect to the dynamics of hand movements, which requires in-depth investigation into feature extraction and machine learning. A computer machine capable of EMG signal data acquisition, time domain feature extraction, pattern recognition implementation, and real-time motor control is still a challenge for researchers to develop prosthetic hands based on EMG pattern recognition [15]–[18]. An embedded system based on a mic","cbCaiqmdxKfhRxx7","https://ap.wps.com/l/cbCaiqmdxKfhRxx7","pdf",641062,2,1,15,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Motivation and EMG challenges\n## Prosthetic hand control approaches\n## Research focus and system concept\n# Materials and Methods\n## Data acquisition and EMG leads\n## Time-domain feature extraction\n## Supervised classifiers and training strategy\n## Embedded Raspberry Pi implementation\n# Results\n## Accuracy performance by class\n## Dataset comparison and statistical significance\n# Conclusion","[{\"question\":\"What is the main goal of this research on the Raspberry Pi system?\",\"answer\":\"To implement EMG pattern recognition on an embedded Raspberry Pi platform so it can recognize hand motion for a smart prosthetic hand as a preliminary study.\"},{\"question\":\"Which EMG features and supervised classifiers are used?\",\"answer\":\"The model uses time-domain features (MAV, RMS, VAR) with a 100 ms window length and applies decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) for classification.\"},{\"question\":\"What performance was achieved in the experiments?\",\"answer\":\"The highest accuracy is reported for the open class at 97.03%, and additional datasets show a significant difference in accuracy with p-value \\u003c 0.05.\"}]","Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion - 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