دورية أكاديمية
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A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.

التفاصيل البيبلوغرافية
العنوان: A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.
المؤلفون: Du J; College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China., Liu Z; College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China., Dong W; College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China., Zhang W; College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China., Miao Z; School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China.
المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Aug 30; Vol. 24 (17). Date of Electronic Publication: 2024 Aug 30.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI, c2000-
مواضيع طبية MeSH: Electromyography*/methods , Wrist Joint*/physiology , Neural Networks, Computer*, Humans ; Range of Motion, Articular/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Male ; Wrist/physiology
مستخلص: Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error ( RMSE ) and average coefficient of determination ( R 2 ). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R 2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.
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معلومات مُعتمدة: 52173101 Natural Science Foundation of China
فهرسة مساهمة: Keywords: human–machine interaction (HMI); long short-term memory neural network (LSTM); surface electromyography (sEMG); temporal convolution network (TCN); wrist kinematics estimation
تواريخ الأحداث: Date Created: 20240914 Date Completed: 20240914 Latest Revision: 20240916
رمز التحديث: 20240916
مُعرف محوري في PubMed: PMC11397992
DOI: 10.3390/s24175631
PMID: 39275542
قاعدة البيانات: MEDLINE
الوصف
تدمد:1424-8220
DOI:10.3390/s24175631