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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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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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Liu C, Li J, Zhang S, Yang H, Guo K. Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation. MICROMACHINES 2022; 13:2047. [PMID: 36557346 PMCID: PMC9782516 DOI: 10.3390/mi13122047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 06/01/2023]
Abstract
Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.
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Affiliation(s)
- Chang Liu
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jiuqiang Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Senhao Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hongbo Yang
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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