北京生物醫(yī)學工程

基于Hill肌肉模型的人體關節(jié)力矩智能預測

Human joint moment prediction based on artificial neural network

作者：熊保平史武翔林昱黃美蘭杜民

單位：福建工程學院數(shù)理學院（福州 350116）福州大學物理與信息工程學院（福州 350116）福建中醫(yī)藥大學附屬人民醫(yī)院 (福州350004) 福建省醫(yī)療器械和醫(yī)藥技術重點實驗室（福州 350116）

關鍵詞：關節(jié)力矩預測；人工神經(jīng)網(wǎng)絡；極限學習機； Hill肌肉模型；輸入變量

分類號：R318.01

出版年·卷·期（頁碼）：2021·40·1（11-23）

摘要：

目的人體關節(jié)力矩是康復評估和人機交互中非常關鍵的因素之一。它可以通過人工神經(jīng)網(wǎng)絡（artificial neural network ，ANN)模型以肌電等信號作為輸入進行預測，但是由于人體結構的復雜性導致缺乏有效方法確定人工神經(jīng)網(wǎng)絡模型的輸入變量。為此本文提出了一種基于Hill肌肉模型獲取關節(jié)力矩神經(jīng)網(wǎng)絡預測最優(yōu)輸入變量的方法。方法利用Hill肌肉模型結合人體幾何學知識建立關節(jié)力矩智能預測輸入輸出關系的數(shù)學模型，把Hill肌肉模型中肌肉纖維長度和收縮速度以及以關節(jié)自由度為支點的肌肉力臂等不可活體測量輸入變量轉換成關節(jié)自由度所關聯(lián)肌肉的肌電信號以及這些肌肉所驅動關節(jié)自由度角度和角速度等可在線測量變量。結果實驗中以本文獲取變量作為極限學習機的輸入，對一位在跑步機上以0.4, 0.5, 0.6, 0.7, 和0.8 m/s等5個不同速度行走的右下肢偏癱患者所獲數(shù)據(jù)進行測試。為了評估本文所提方法智能預測的泛化能力，實驗在兩個不同的泛化水平下進行，它們分別是只把前面三個低速數(shù)據(jù)（0.4, 0.5, 和0.6m/s）和全部五個速度的數(shù)據(jù)用于神經(jīng)網(wǎng)絡的訓練并預測所有速度下的關節(jié)力矩值。實驗是通過預測值與反向動力學計算值之間的歸一化絕對誤差和互相關系數(shù)評估。結果表明，本文所提輸入變量與其他關節(jié)角和角速度作為輸入方法相比預測關節(jié)力矩值更精準，除右踝關節(jié)內(nèi)外翻展外其他關節(jié)力矩預測結果的最大歸一化絕對誤差為12.93%，最小平均互相關系數(shù)為0.89。結論該方法比目前通用多體反向動力學的輸入變量少且可實現(xiàn)關節(jié)力矩值的在線預測，可為運動康復中實時步態(tài)分析和外骨骼機器人控制提供技術支持。

Objective Human joint moment is one of the most important factors in rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network model. However, challenge remains as lack of effective methodologies to determine the input variables for the artificial neural network (ANN) model. Methods This study develops a novel method to determine the optimal input variables to the ANN based on the Hill muscle model for estimating lower extremity joint moments. In this method, we translate the muscle-tendon moment arm, velocity and length in the Hill muscle model to the online measurable variables, i.e. muscle span joints’ angles and angular velocities signals. We use moment-associated muscles’ electromyography (EMG) signals together with these variables of the muscle as the inputs to the ANN for joint moment prediction. Results The method is tested on the experimental data collected from a subject with a highly functional hemiplegic stroke，who is walking on a treadmill with different speeds, i.e. 0.4, 0.5, 0.6, 0.7, and 0.8 m/s. We first use the data collected in all speeds as the training data to predict the joint moment. To evaluate the generalization ability of our method, we then repeat the same test using only low speed (0.4, 0.5 and 0.6 m/s) data to train the ANN model for the joint moment prediction at all speeds. The accuracy of prediction is evaluated by using the normalized root-mean-square error and cross correlation coefficient between the predicted joint moment and multi-body dynamics moment. Our results suggest that our method can predict joint moments with a higher accuracy than those obtained by using other joint angles and angular velocities as inputs, except the right ankle inversion-eversion, the maximum normalized root mean square error of each joint moment of the lower limb was 12.93%, and the minimum average cross-correlation coefficient was 0.89. Conclusion The proposed method provides us with a useful tool to predict joint moment using online measurable variables, which uses less variables than the commonly used multi-body dynamics approach with a comparable accuracy. This method may facilitate the research on real-time gait analysis and exoskeleton robot control in motor rehabilitation.

參考文獻：

[1] Ardestani MM, Zhang X, Wang L, 等. Human lower extremity joint moment prediction: A wavelet neural network approach[J]. Expert Systems with Applications, 2014, 41(9): 4422–4433.

[2] Park H S, Peng Q, Zhang L Q. A Portable Telerehabilitation System for Remote Evaluations of Impaired Elbows in Neurological Disorders[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008, 16(3): 245–254.

[3] Zhang S, Guo S, Gao B, 等. Design of a Novel Telerehabilitation System with a Force-Sensing Mechanism[J]. Sensors, 2015, 15(5): 11511–11527.

[4] Schmidt A. Movement pattern recognition in basketball free-throw shooting[J]. Human Movement Science, 2012, 31(2): 360–382.

[5] Pfeiffer M, Hohmann A. Applications of neural networks in training science[J]. Human Movement Science, 2012, 31(2): 344–359.

[6] Joshi D, Mishra A, Anand S. ANFIS based knee angle prediction: An approach to design speed adaptive contra lateral controlled AK prosthesis[J]. Applied Soft Computing, 2011, 11(8): 4757–4765.

[7] Rupérez M J, Martín-guerrero J D, Monserrat C, 等. Artificial neural networks for predicting dorsal pressures on the foot surface while walking[J]. Expert Systems with Applications, 2012, 39(5): 5349–5357.

[8] Burnie J, Brodie D A. Isokinetics in the assessment of rehabilitation: a case report[J]. Clinical Biomechanics, 1986, 1(3): 140–146.

[9] Callaghan M J, Mccarthy C J, Al-omar A, 等. The reproducibility of multi-joint isokinetic and isometric assessments in a healthy and patient population[J]. Clinical Biomechanics, 2000, 15(9): 678–683.

[10] R, Happee, and, et al. The control of shoulder muscles during goal directed movements, an inverse dynamic analysis[J]. Journal of Biomechanics, 1995.

[11] Kim Y-H, Phuong B T T. Estimation of Joint Moment and Muscle Force in Lower Extremity During Sit-to-Stand Movement by Inverse Dynamics Analysis and by Electromyography[J]. Transactions of the Korean Society of Mechanical Engineers A, 2010, 34(10): 1345–1350.

[12] Lloyd D G, Besier T F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo[J]. Journal of Biomechanics, 2003, 36(6): 765–776.

[13] Meyer A J, Patten C, Fregly B J. Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry[J]. PLOS ONE, 2017, 12(7): e0179698.

[14] Sartori M, Reggiani M, Farina D, 等. EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity[J]. PLOS ONE, 2012, 7(12): e52618.

[15] Uchiyama T, Bessho T, Akazawa K. Static torque–angle relation of human elbow joint estimated with artificial neural network technique[J]. Journal of Biomechanics, 1998, 31(6): 545–554.

[16] Luh J-J, Chang G-C, Cheng C-K, 等. Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model[J]. Journal of Electromyography and Kinesiology, 1999, 9(3): 173–183.

[17] Song R, Tong K Y. Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations[J]. Medical and Biological Engineering and Computing, 2005, 43(4): 473–480.

[18] Hahn M E. Feasibility of estimating isokinetic knee torque using a neural network model[J]. Journal of Biomechanics, 2007, 40(5): 1107–1114.

[19] Ardestani M M, Zhang X, Wang L, 等. Human lower extremity joint moment prediction: A wavelet neural network approach[J]. Expert Systems with Applications, 2014, 41(9): 4422–4433.

[20] Kipp K , Giordanelli M , Geiser C . Predicting net joint moments during a weightlifting exercise with a neural network model[J]. Journal of Biomechanics, 2018:S002192901830294X.

[21] May R , Dandy G , Maier H . Review of Input Variable Selection Methods for Artificial Neural Networks[M]// Artificial Neural Networks - Methodological Advances and Biomedical Applications. InTech, 2011.

[22] Hill Archibald Vivian. The heat of shortening and the dynamic constants of muscle[J]. Proceedings of the Royal Society of London. Series B - Biological Sciences, 1938, 126(843): 136–195.

[23] Kumar D, Rudolph K S, Manal K T. EMG-driven modeling approach to muscle force and joint load estimations: Case study in knee osteoarthritis[J]. Journal of Orthopaedic Research, , 30(3): 377–383.

[24] Feng G, Huang G B, Lin Q, 等. Error minimized extreme learning machine with growth of hidden nodes and incremental learning.[J]. IEEE Transactions on Neural Networks, 2009, 20(8): 1352–1357.

[25] Huang G B , Zhu Q Y , Siew C K . Extreme Learning Machine: Theory and Applications[J]. Neurocomputing, 2006, 70(1-3).

[26] Huang G-B, Chen L. Enhanced random search based incremental extreme learning machine[J]. Neurocomputing, 2008, 71(16): 3460–3468.

[27] Huang G B, Chen L. Letters: Convex incremental extreme learning machine[J]. Neurocomputing, 2007, 70(16): 3056–3062.

[28] Reinbolt J A, Schutte J F, Fregly B J, 等. Determination of patient-specific multi-joint kinematic models through two-level optimization[J]. Journal of Biomechanics, 2005, 38(3): 621–626.

[29] Hug F. Can muscle coordination be precisely studied by surface electromyography?[J]. Journal of Electromyography and Kinesiology, 2011, 21(1): 1–12.

[30] Menegaldo L L, Fleury A de T, Weber H I. Moment arms and musculotendon lengths estimation for a three-dimensional lower-limb model[J]. Journal of Biomechanics, 2004, 37(9): 1447–1453.

[31] Sartori M, Reggiani M, Bogert A J van den, 等. Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines[J]. Journal of Biomechanics, 2012, 45(3): 595–601.

[32] An K N, Takahashi K, Harrigan T P, 等. Determination of muscle orientations and moment arms[J]. Journal of Biomechanical Engineering, 1984, 106(3): 280–282.

[33] Tanzil S M S, Hoiles W, Krishnamurthy V. Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach[J]. IEEE Access, 2017, 5(99): 5870–5881.

[34] Yang Y, Wu Q M J, Wang Y. Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction[J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2016, PP(99): 1–15.

[35] Liu Y, Shih S-M, Tian S-L, 等. Lower extremity joint torque predicted by using artificial neural network during vertical jump[J]. Journal of Biomechanics, 2009, 42(7): 906–911.

[36] Zhang F, Li P, Hou Z-G, 等. sEMG-based continuous estimation of joint angles of human legs by using BP neural network[J]. Neurocomputing, 2012, 78(1): 139–148.

[37] Henriksen M, Aaboe J, Bliddal H, 等. Biomechanical characteristics of the eccentric Achilles tendon exercise[J]. Journal of Biomechanics, 2009, 42(16): 2702–2707.

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