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Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees
المؤلفون المشاركون
Li, Guanglin
Geng, Yanjuan
Samuel, Oluwarotimi Williams
Wei, Yue
المصدر
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-04-24
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios.
However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control.
In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses.
The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees.
The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.).
The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Geng, Yanjuan& Samuel, Oluwarotimi Williams& Wei, Yue& Li, Guanglin. 2017. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Geng, Yanjuan…[et al.]. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Geng, Yanjuan& Samuel, Oluwarotimi Williams& Wei, Yue& Li, Guanglin. Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1137416
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1137416
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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