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Wu D, Tian P, Zhang S, Wang Q, Yu K, Wang Y, Gao Z, Huang L, Li X, Zhai X, Tian M, Huang C, Zhang H, Zhang J. A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring. SENSORS (BASEL, SWITZERLAND) 2024; 24:3818. [PMID: 38931601 PMCID: PMC11207591 DOI: 10.3390/s24123818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 05/31/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.
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Affiliation(s)
- Dantong Wu
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Tian
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuai Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
| | - Qihang Wang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
| | - Kang Yu
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
| | - Yunfeng Wang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhixing Gao
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Huang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangyu Li
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingchen Zhai
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Meng Tian
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengjun Huang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiying Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Zhang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China; (D.W.); (P.T.); (S.Z.); (Q.W.); (K.Y.); (Y.W.); (Z.G.); (L.H.); (X.L.); (X.Z.); (M.T.); (C.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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Dynamic gripping force estimation and reconstruction in EMG-based human-machine interaction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Mao H, Fang P, Zheng Y, Tian L, Li X, Wang P, Peng L, Li G. Continuous grip force estimation from surface electromyography using generalized regression neural network. Technol Health Care 2023; 31:675-689. [PMID: 36120747 DOI: 10.3233/thc-220283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Grip force estimation is highly required in realizing flexible and accurate prosthetic control. OBJECTIVE This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees. METHODS Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE). RESULTS The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training. CONCLUSIONS The proposed method has the potential for precise force control of prosthetic hands.
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Affiliation(s)
- He Mao
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
| | - Peng Fang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
| | - Yue Zheng
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
| | - Lan Tian
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
| | - Xiangxin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
| | - Pu Wang
- Department of Rehabilitation Medicine, The 7th Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Liang Peng
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Engineering Laboratory of Neural Rehabilitation Technology, Shenzhen, Guangdong, China
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Xu B, Zhang K, Yang X, Liu D, Hu C, Li H, Song A. Natural grasping movement recognition and force estimation using electromyography. Front Neurosci 2022; 16:1020086. [DOI: 10.3389/fnins.2022.1020086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user’s experience and acceptance.
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Chihi I, Sidhom L, Kamavuako EN. Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals. BIOSENSORS 2022; 12:bios12020117. [PMID: 35200377 PMCID: PMC8870134 DOI: 10.3390/bios12020117] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 05/27/2023]
Abstract
This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein-Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.
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Affiliation(s)
- Ines Chihi
- Department of Engineering, Campus Kirchberg, Faculté des Sciences, des Technologies et de Médecine, Université du Luxembourg, 1359 Luxembourg, Luxembourg
| | - Lilia Sidhom
- Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, Tunisia;
| | - Ernest Nlandu Kamavuako
- Department of Engineering, King’s College London, London WC2R 2LS, UK;
- Faculté de Médecine, Université de Kindu, Kindu, Democratic Republic of the Congo
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Avian C, Prakosa SW, Faisal M, Leu JS. Estimating finger joint angles on surface EMG using Manifold Learning and Long Short-Term Memory with Attention mechanism. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103099] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Sun Z, Li J, Wu J, Zou X, Ho CT, Liang L, Yan X, Zhou X. Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics. FOOD SCIENCE AND HUMAN WELLNESS 2021. [DOI: 10.1016/j.fshw.2021.02.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Chen Y, Ma K, Yang L, Yu S, Cai S, Xie L. Trunk compensation electromyography features purification and classification model using generative adversarial network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8846021. [PMID: 33456452 PMCID: PMC7785339 DOI: 10.1155/2020/8846021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/22/2020] [Accepted: 12/15/2020] [Indexed: 11/18/2022]
Abstract
Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects' upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation (p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.
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Martinez IJR, Mannini A, Clemente F, Cipriani C. Online Grasp Force Estimation From the Transient EMG. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2333-2341. [PMID: 32894718 DOI: 10.1109/tnsre.2020.3022587] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Myoelectric upper limb prostheses are controlled using information from the electrical activity of residual muscles (i.e. the electromyogram, EMG). EMG patterns at the onset of a contraction (transient phase) have shown predictive information about upcoming grasps. However, decoding this information for the estimation of the grasp force was so far overlooked. In a previous offline study, we proved that the transient phase of the EMG indeed contains information about the grasp force and determined the best algorithm to extract this information. Here we translated those findings into an online platform to be tested with both non-amputees and amputees. The platform was tested during a pick and lift task (tri-digital grasp) with light objects (200 g - 1 kg), for which fine control of the grasp force is more important. Results show that, during this task, it is possible to estimate the target grasp force with an absolute error of 2.06 (1.32) % and 2.04 (0.49) % the maximum voluntary force for non-amputee and amputees, respectively, using information from the transient phase of the EMG. This approach would allow for a biomimetic regulation of the grasp force of a prosthetic hand. Indeed, the users could contract their muscles only once before the grasp begins with no need to modulate the grasp force for the whole duration of the grasp, as required with continuous classifiers. These results pave the way to fast, intuitive and robust myoelectric controllers of limb prostheses.
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Zhang Y, Xia C. A preliminary study of classification of upper limb motions and forces based on mechanomyography. Med Eng Phys 2020; 81:97-104. [PMID: 32507673 DOI: 10.1016/j.medengphy.2020.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 05/06/2020] [Accepted: 05/13/2020] [Indexed: 10/24/2022]
Abstract
Rehabilitation training is essential for patients who have a history of certain illnesses, such as stroke. As a crucial part of rehabilitation training, upper limb training involves such key factors as upper limb motions and forces. This study investigated three upper limb motions (elbow flexion of 135°, Motion 1; shoulder flexion of 90°, Motion 2; and shoulder abduction of 90°, Motion 3) and various forces (muscle Force 0, no force; holding one 1.4 kg dumbbell, muscle Force 1; holding one 2.4 kg dumbbell, muscle Force 2) in combination to evaluate nine motion patterns. These patterns were completed by twelve healthy volunteers. Mechanomyography (MMG) measurements of the biceps brachii (Channel 1), triceps (Channel 2), and deltoid (Channel 3) muscles were collected. These were subsequently divided into signal segments corresponding to each of the motions using a segmentation method based on average energy. After extracting time-domain features and wavelet packet energy features, support vector machine analysis (SVM) was used for the classification of the upper limb motions and forces based on the MMG measurements. Channel 2 and Channel 3 were shown to play an important role in the classification of upper limb motions, and Channel 1 played a role in the classification of the forces. These results demonstrate that collection of MMG measurements from the three muscles is feasible and suggest a foundation for further studies in which rehabilitation training is evaluated based on MMG measurements.
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Affiliation(s)
- Yue Zhang
- Department of Mechanical Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Chunming Xia
- Department of Mechanical Engineering, East China University of Science and Technology, Shanghai 200237, China.
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Martinez IJR, Mannini A, Clemente F, Sabatini AM, Cipriani C. Grasp force estimation from the transient EMG using high-density surface recordings. J Neural Eng 2020; 17:016052. [DOI: 10.1088/1741-2552/ab673f] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245343] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.
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