Comparing the efficiency of recurrent neural networks to EMG-based continuous estimation of the elbow angle (original) (raw)
References
Ahmadizadeh C, Khoshnam M, Menon C (2021) Human machine interfaces in upper-limb prosthesis control: a survey of techniques for preprocessing and processing of biosignals. IEEE Signal Process Mag 38(4):12–22 Google Scholar
Jiang N, Chen C, He J, Meng J, Pan L, Su S, Zhu X (2023) Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review. Natl Sci Rev 10(5):1–21 Google Scholar
Kiguchi K, Hayashi Y (2012) An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans Syst Man Cybern Part B (Cybern) 42(4):1064–1071 Google Scholar
Batzianoulis I, Krausz NE, Simon AM, Hargrove L, Billard A (2018) Decoding the grasping intention from electromyography during reaching motions. J Neuroeng Rehabil 15(1):1–13 Google Scholar
Lu Z, Tong K-Y, Zhang X, Li S, Zhou P (2018) Myoelectric pattern recognition for controlling a robotic hand: a feasibility study in stroke. IEEE Trans Biomed Eng 66(2):365–372 Google Scholar
Sun T, Hu Q, Gulati P, Atashzar SF (2021) Temporal dilation of deep LSTM for agile decoding of sEMG: application in prediction of upper-limb motor intention in neurorobotics. IEEE Robot Autom Lett 6:6212–6219 Google Scholar
Gulati P, Hu Q, Atashzar SF (2021) Toward deep generalization of peripheral EMG-based human-robot interfacing: a hybrid explainable solution for neurorobotic systems. IEEE Robot Autom Lett 6(2):2650–2657 Google Scholar
Wang Y, Wu Q, Dey N, Fong S, Ashour AS (2020) Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybern Biomed Eng 40(3):987–1001 Google Scholar
Abbaspour S, Lindén M, Gholamhosseini H, Naber A, Ortiz-Catalan M (2020) Evaluation of surface EMG-based recognition algorithms for decoding hand movements. Med Bio Eng Comput 58(1):83–100 Google Scholar
Wu L, Zhang X, Zhang X, Chen X, Chen X (2021) Metric learning for novel motion rejection in high-density myoelectric pattern recognition. Knowl-Based Syst 227:107165 Google Scholar
Castiblanco JC, Ortmann S, Mondragon IF, Alvarado-Rojas C, Jöbges M, Colorado JD (2020) Myoelectric pattern recognition of hand motions for stroke rehabilitation. Biomed Signal Process Control 57:101737 Google Scholar
Jia G, Lam H-K, Liao J, Wang R (2020) Classification of electromyographic hand gesture signals using machine learning techniques. Neurocomputing 401:236–248 Google Scholar
Jiang N, Englehart KB, Parker PA (2009) Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal IEEE transactions on. Biomed Eng 56(4):1070–1080 Google Scholar
Hahne JM, Markovic M, Farina D (2017) User adaptation in myoelectric man-machine interfaces. Sci Rep 7(1):4437–4446 Google Scholar
Zhang L, Li Z, Hu Y, Smith C, Farewik EMG, Wang R (2020) Ankle joint torque estimation using an emg-driven neuromusculoskeletal model and an artificial neural network model. IEEE Trans Autom Sci Eng 18(2):564–573 Google Scholar
Huang Y, Chen K, Zhang X, Wang K, Ota J (2020) Joint torque estimation for the human arm from sEMG using backpropagation neural networks and autoencoders. Biomed Signal Process Control 62:102051 Google Scholar
Gui K, Liu H, Zhang D (2019) A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton. IEEE/ASME Trans Mech 24(2):483–494 Google Scholar
Yang W, Yang D, Liu Y, Liu H (2019) Decoding simultaneous multi-DOF wrist movements from raw EMG signals using a convolutional neural network. IEEE Trans Hum-Mach Syst 49(5):411–420 Google Scholar
Ma Y, Jiang S, Mithraratne K, Wilson N, Yu Y, Zhang Y (2021) The effect of musculoskeletal model scaling methods on ankle joint kinematics and muscle force prediction during gait for children with cerebral palsy and equinus gait. Comput Biol Med 134:104436 Google Scholar
Chen Y, Dai C, Chen W (2020) Cross-comparison of EMG-to-force methods for multi-DoF finger force prediction using one-DoF training. IEEE Access 8:13958–13968 Google Scholar
Kwon S, Kim J (2011) Real-time upper limb motion estimation from surface electromyography and joint angular velocities using an artificial neural network for human–machine cooperation. IEEE Trans Inf Technol Biomed 15(4):522–530 Google Scholar
Liang J, Shi Z, Zhu F, Chen W, Chen X, Li Y (2021) Gaussian process autoregression for joint angle prediction based on sEMG signals. Front Public Health 9:567 Google Scholar
Xie H, Li G, Zhao X, Li F (2020) Prediction of limb joint angles based on multi-source signals by GS-GRNN for exoskeleton wearer. Sensors 20(4):1104 Google Scholar
Qin Z, Stapornchaisit S, He Z, Yoshimura N, Koike Y (2021) Multi-joint angles estimation of forearm motion using a regression model. Front Neurorobotics 5:103 Google Scholar
Wang J, Wang L, Miran SM, Xi X, Xue A (2019) Surface electromyography based estimation of knee joint angle by using correlation dimension of wavelet coefficient. IEEE Access 7:60522–60531 Google Scholar
Nasr A, Bell S, He J, Whittaker RL, Jiang N, Dickerson CR, McPhee J (2021) MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning. J Neural Eng 18(4):0460–0463 Google Scholar
Wiedemann LG, Jayaneththi VR, Kimpton J, Chan A, Müller MA, Hogan A, Lim E, Wilson NC, McDaid AJ (2018) Neuromuscular characterisation in Cerebral Palsy using hybrid Hill-type models on isometric contractions. Comput Biol Med 103:269–276 Google Scholar
Zeng Y, Yang J, Yin Y (2019) Gaussian process-integrated state space model for continuous joint angle prediction from EMG and interactive force in a human-exoskeleton system. Appl Sci 9(8):1711 Google Scholar
Kawase T, Sakurada T, Koike Y, Kansaku K (2017) A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements. J Neural Eng 14(1):1–12 Google Scholar
Zhang F, Li P, Hou Z-G, Lu Z, Chen Y, Li Q, Tan M (2012) sEMG-based continuous estimation of joint angles of human legs by using BP neural network. Neurocomputing 78(1):139–148 Google Scholar
Hahne JM, Biessmann F, Jiang N, Rehbaum H, Farina D, Meinecke FC, Müller KR, Parra LC (2014) Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22(2):269–279 Google Scholar
Triwiyanto T, Wahyunggoro O, Nugroho HA, Herianto H (2017) An investigation into time domain features of surface electromyography to estimate the elbow joint angle. Adv Electr Electron Eng 15(3):448–458 Google Scholar
Wahyunggoro O, Nugroho HA (2018) Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography. J Mech Eng Sci 12(3):3786–3796 Google Scholar
Song Z, Zhang S (2016) Preliminary study on continuous recognition of elbow flexion/extension using sEMG signals for bilateral rehabilitation. Sensors 16(10):1739 Google Scholar
Chen Y, Zhao X, Han J (2013) Hierarchical projection regression for online estimation of elbow joint angle using EMG signals. Neural Comput Appl 23(3–4):1129–1138 Google Scholar
Xiao F, Wang Y, Gao Y, Zhu Y, Zhao J (2018) Continuous estimation of joint angle from electromyography using multiple time-delayed features and random forests. Biomed Signal Process Control 39:303–311 Google Scholar
Li Z, Guan X, Zou K, Xu C (2019) Estimation of knee movement from surface emg using random forest with principal component analysis. Electronics 9(1):43 Google Scholar
Hwang H-J, Hahne JM, Müller K-R (2014) Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom. J Neural Eng 11(5):056008 Google Scholar
Triwiyanto T, Wahyunggoro O, Nugroho HA, Herianto H (2018) Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature. Comput Electr Eng 71:284–293 Google Scholar
Artemiadis PK, Kyriakopoulos KJ (2010) EMG-based control of a robot arm using low-dimensional embeddings. IEEE Trans Rob 26(2):393–398 Google Scholar
Bao T, Zhao Y, Zaidi SAR, Xie S, Yang P, Zhang Z (2021) A deep Kalman filter network for hand kinematics estimation using sEMG. Pattern Recogn Lett 143:88–94 Google Scholar
Gao Y, Luo Y, Zhao J, Li Q (2019) sEMG-angle estimation using feature engineering techniques for least square support vector machine. Technol Health Care 27(S1):31–46 Google Scholar
Yang C, Xi X, Chen S, Miran SM, Hua X, Luo Z (2019) SEMG-based multifeatures and predictive model for knee-joint-angle estimation. AIP Adv 9(9):095042 Google Scholar
Jiang N, Muceli S, Graimann B, Farina D (2013) Effect of arm position on the prediction of kinematics from EMG in amputees. Med Biol Eng Comput 51(1–2):143–151 Google Scholar
Jiang N, Vest-Nielsen JLG, Muceli S, Farina D (2012) EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees. J Neuroeng Rehabil 9(1):1–11 Google Scholar
Ameri A, Scheme EJ, Kamavuako EN, Englehart KB, Parker PA (2013) Real-time, simultaneous myoelectric control using force and position-based training paradigms. IEEE Trans Biomed Eng 61(2):279–287 Google Scholar
Liu MM, Herzog W, Savelberg HHCM (1999) Dynamic muscle force predictions from EMG: an artificial neural network approach. J Electromyograp Kinesiol 9(6):391–400 Google Scholar
Koike Y, Kawato M (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biol Cybern 73(4):291–300 Google Scholar
Nielsen JLG, Holmgaard S, Jiang N, Englehart KB, Farina D, Parker PA (2010) Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training. IEEE Trans Biomed Eng 58(3):681–688 Google Scholar
Raj R, Sivanandan KS (2016) Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters. J Back Musculoskelet Rehabil 30(3):515–525 Google Scholar
Tong L, Zhang F, Hou Z-G, Wang W, Peng L (2015) BP-AR-Based human joint angle estimation using multi-channel sEMG. Int J Robot Autom 30(3):227–237 Google Scholar
Xia P, Hu J, Peng Y (2017) EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks. Artif Organs 42(5):E67–E77 Google Scholar
Liu J, Kang SH, Xu D, Ren Y, Lee SJ, Zhang L-Q (2017) EMG-based continuous and simultaneous estimation of arm kinematics in able-bodied individuals and stroke survivors. Front Neurosci 11:480 Google Scholar
Chen J, Zhang X, Cheng Y, Xi N (2018) Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks. Biomed Signal Process Control 40:335–342 Google Scholar
Batayneh W, Abdulhay E, Alothman M (2020) Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques. Heliyon 6(4):e03669 Google Scholar
Wang J, Wang L, Xi X, Miran SM, Xue A (2020) Estimation and correlation analysis of lower limb joint angles based on surface electromyography. Electronics 9(4):556 Google Scholar
Ma X, Liu Y, Song Q, Wang C (2020) Continuous estimation of knee joint angle based on surface electromyography using a long short-term memory neural network and time-advanced feature. Sensors 20(17):4966 Google Scholar
Tang G, Sheng J, Wang D, Men S (2020) Continuous estimation of human upper limb joint angles by using PSO-LSTM model. IEEE Access 9:17986–17997 Google Scholar
Yang Z, Guo S, Liu Y, Hirata H, Tamiya T (2020) An intention-based online bilateral training system for upper limb motor rehabilitation. Microsyst Technol 27(1):211–222 Google Scholar
Batayneh W, Abdulhay E, Alothman M (2021) Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics. Digital Commun Netw 8:162–173 Google Scholar
Ma C, Lin C, Samuel OW, Xu L, Li G (2020) Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach. Biomed Signal Process Control 61:102024 Google Scholar
Gautam A, Panwar M, Biswas D, Acharyya A (2020) MyoNet: A transfer-learning-based LRCN for lower limb movement recognition and knee joint angle prediction for remote monitoring of rehabilitation progress from sEMG. IEEE J Transl Eng Health Med 8:1–10 Google Scholar
Lunardini F, Casellato C, d’Avella A, Sanger TD, Pedrocchi A (2016) Robustness and reliability of synergy-based myocontrol of a multiple degree of freedom robotic arm. IEEE Trans Neural Syst Rehabil Eng 24(9):940–950 Google Scholar
Muceli S, Jiang N, Farina D (2013) Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms. IEEE Trans Neural Syst Rehabil Eng 22(3):623–633 Google Scholar
Boussaada Z, Curea O, Remaci A, Camblong H, Mrabet Bellaaj N (2018) A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies 11(3):620 Google Scholar
Lei Z (2019) An upper limb movement estimation from electromyography by using BP neural network. Biomed Signal Process Control 49:434–439 Google Scholar
Zhang Q, Liu R, Chen W, Xiong C (2017) Simultaneous and continuous estimation of shoulder and elbow kinematics from surface emg signals. Front Neurosci 11:280 Google Scholar
Tang Z, Yu H, Cang S (2015) Impact of load variation on joint angle estimation from surface EMG signals. IEEE Trans Neural Syst Rehabil Eng 24(12):1342–1350 Google Scholar
Chen Y, Yu S, Ma K, Huang S, Li G, Cai S, Xie L (2019) A continuous estimation model of upper limb joint angles by using surface electromyography and deep learning method. IEEE Access 7:174940–174950 Google Scholar
Bianchi FM, Maiorino E, Kampffmeyer MC, Rizzi A, Jenssen R (2017) An overview and comparative analysis of recurrent neural networks for short term load forecasting arXiv preprint arXiv:1705.04378:
Wang H, Song G (2014) Innovative NARX recurrent neural network model for ultra-thin shape memory alloy wire. Neurocomputing 134:289–295 Google Scholar
Vlachas PR, Pathak J, Hunt BR, Sapsis TP, Girvan M, Ott E, Koumoutsakos P (2020) Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics. Neural Netw 126:191–217 Google Scholar
Sun X, Li T, Li Y, Li Q, Huang Y, Liu J (2018) Recurrent neural system with minimum complexity: a deep learning perspective. Neurocomputing 275:1333–1349 Google Scholar
George KS, Sivanandan KS, Mohandas KP (2018) Estimation of elbow angle using surface electromyographic signals. J Intell Fuzzy Syst 34(6):4191–4201 Google Scholar
Achanta S, Gangashetty SV (2017) Deep Elman recurrent neural networks for statistical parametric speech synthesis. Speech Commun 93:31–42 Google Scholar
Merletti R, Hermens H (2000) Introduction to the special issue on the SENIAM European concerted action journal of electromyography and kinesiology: official journal of the international society of electrophysiological. Kinesiology 10(5):283–286 Google Scholar
Davarinia F, Maleki A (2022) SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement. Biomed Signal Process Control 71:103222 Google Scholar
Jiang N, Vujaklija I, Rehbaum H, Graimann B, Farina D (2014) Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans Neural Syst Rehabil Eng 22(3):549–558 Google Scholar
Li X, Wong W, Lamoureux EL, Wong TY (2012) Are linear regression techniques appropriate for analysis when the dependent (outcome) variable is not normally distributed? Invest Ophthalmol Vis Sci 53(6):3082–3083 Google Scholar
Ferreira AA, Ludermir TB, de Aquino RR (2012) Comparing recurrent networks for time-series forecasting, In: The 2012 International Joint Conference on Neural Networks (IJCNN), (IEEE2012), pp 1–8.
Akhtar A, Aghasadeghi N, Hargrove L, Bretl T (2017) Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses. J Electromyogr Kinesiol 35:86–94 Google Scholar
Li K, Zhang J, Liu X, Zhang M (2019) Estimation of continuous elbow joint movement based on human physiological structure. Biomed Eng Online 18(1):31 Google Scholar
Tampuu A, Matiisen T, Ólafsdóttir HF, Barry C, Vicente R (2019) Efficient neural decoding of self-location with a deep recurrent network. PLoS Comput Biol 15(2):e1006822 Google Scholar
Siavashani AG, Yousefi-Koma A, Vedadi A (2023) Estimation and early prediction of grip force based on sEMG signals and deep recurrent neural networks. J Braz Soc Mech Sci Eng 45(5):264 Google Scholar
Zheng K, Liu S, Yang J, Al-Selwi M, Li J (2022) sEMG-based continuous hand action prediction by using key state transition and model pruning. Sensors 22(24):9949 Google Scholar
Kim D, Koh K, Oppizzi G, Baghi R, Lo L-C, Zhang C, Zhang L-Q (2021) EMG-based simultaneous estimations of joint angle and torque during hand interactions with environments IEEE Trans Biomed Eng
Ma C, Lin C, Samuel OW, Guo W, Zhang H, Greenwald S, Xu L, Li G (2021) A bi-directional LSTM network for estimating continuous upper limb movement from surface electromyography. IEEE Robot Autom Lett 6(4):7217–7224 Google Scholar
Xiong D, Zhang D, Zhao X, Zhao Y (2021) Deep learning for EMG-based human-machine interaction: a review. IEEE/CAA J Autom Sin 8(3):512–533 Google Scholar
Ameri A, Akhaee MA, Scheme E, Englehart K (2018) Real-time, simultaneous myoelectric control using a convolutional neural network. PLoS ONE 13(9):e0203835 Google Scholar
Bao T, Zaidi SAR, Xie S, Yang P, Zhang Z-Q (2020) A CNN-LSTM hybrid model for wrist kinematics estimation using surface electromyography. IEEE Trans Instrum Meas 70:1–9 Google Scholar
Sun T, Hu Q, Libby J, Atashzar SF (2021) Deep heterogeneous dilation of LSTM for transient-phase gesture prediction through high-density electromyography: application in neurorobotics bioRxiv
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444 Google Scholar
Coskun M, Yildirim O, Demir Y, Acharya UR (2022) Efficient deep neural network model for classification of grasp types using sEMG signals. J Ambient Intell Human Comput 13(9):4437–4450 Google Scholar
Wang H, Qin C, Zhang Y, Fu Y (2020) Neural pruning via growing regularization arXiv preprint arXiv:2012.09243:
Jin Q, Yang L, Liao Z (2020) Adabits: neural network quantization with adaptive bit-widths, In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020), pp 2146–2156
Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network arXiv preprint arXiv:1503.02531:
Chen Z, Zhang L, Cao Z, Guo J (2018) Distilling the knowledge from handcrafted features for human activity recognition. IEEE Trans Industr Inf 14(10):4334–4342 Google Scholar
Farrell TR, Weir RF (2007) The optimal controller delay for myoelectric prostheses. IEEE Trans Neural Syst Rehabil Eng 15(1):111–118 Google Scholar
Sarasola-Sanz A, Irastorza-Landa N, López-Larraz E, Shiman F, Spüler M, Birbaumer N, Ramos-Murguialday A (2018) Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients. Sci Rep 8(1):16688 Google Scholar
Miller LA, Stubblefield KA, Lipschutz RD, Lock BA, Kuiken TA (2008) Improved myoelectric prosthesis control using targeted reinnervation surgery: a case series. IEEE Trans Neural Syst Rehabil Eng 16(1):46–50 Google Scholar