Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction
المؤلفون المشاركون
Yu, Mei
Li, Guanglin
Huang, Pingao
Wang, Hui
Wang, Yuan
Liu, Zhiyuan
Samuel, Oluwarotimi Williams
Li, Xiangxin
Chen, Shixiong
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-04-14
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions.
Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems.
Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition.
In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents.
More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements.
Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks.
The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm.
Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased.
These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Pingao& Wang, Hui& Wang, Yuan& Liu, Zhiyuan& Samuel, Oluwarotimi Williams& Yu, Mei…[et al.]. 2020. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Pingao…[et al.]. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Pingao& Wang, Hui& Wang, Yuan& Liu, Zhiyuan& Samuel, Oluwarotimi Williams& Yu, Mei…[et al.]. Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139487
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139487
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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