Document Type : Full Research Paper


1 Ph.D. Candidate, Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran

2 Associate Professor, Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran

3 Assistant Professor, Electrical and Computer Engineering Department, Semnan University, Semnan, Iran

4 Assistant Professor, Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran


The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall movement trajectory. Therefore, it is possible to interpret complex movements, learning, adaptability and other features of the motion control system using submovements. The purpose of this study is predicting and generating planar reaching movements using a realistic model similar to the actual mechanism of human movement and based on the submovement. The data used consists of different replications of four types of planar movement Performed by three healthy subjects. After the preprocessing and phasing, the movements decomposed to minimum-jerk submovement. In the next step, the training of three distinct neural networks was carried out to learn the submovement parameters including the amplitude, duration, and initiation time. Finally, the ANNs were combined to form a closed-loop model that generated accurate reaching movements based on the error correction. The target access rate for all predicted movements by the closed loop model was 100%. Also, the mean distance to the target, the VAF, and the mean MSE error between the predicted and main movement trajectory showed that the predicted movements are a good approximation of the main movements. The results showed that when trained neural networks with submovements, were placed in a closed loop model, they were able to predict proper submovements for complete access to targets due to the compensation of propagated errors from the previous steps. The results of this study can be used to improve motor rehabilitation methods.


[1]   C. T. Freeman, “Upper Limb Electrical Stimulation Using Input-Output Linearization and Iterative Learning Control,” Ieee Trans. Control Syst. Technol., vol. 23, no. 4, pp. 1546–1554, 2015.
[2]   عاشق طوسی، مهناز؛, “پیش بینی وضعیت مفصل دیستال دست با استفاده از اطلاعات کینماتیک و سیگنال الکترومایوگرام ارادی بر اساس سینرژی،,” دانشگاه صنعتی امیرکبیر, 1389.
[3]   Z. Li, D. Guiraud, D. Andreu, A. Gelis, C. Fattal, and M. Hayashibe, “Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients,” Int. J. Neural Syst., vol. 28, no. 06, p. 1750063, Aug. 2018.
[4]   K. Gant, S. Guerra, L. Zimmerman, B. A. Parks, N. W. Prins, and A. Prasad, “EEG-controlled functional electrical stimulation for hand opening and closing in chronic complete cervical spinal cord injury,” Biomed. Phys. Eng. Express, vol. 4, no. 6, p. 065005, Sep. 2018.
[5]   T. Street and C. Singleton, “A clinically meaningful training effect in walking speed using functional electrical stimulation for motor-incomplete spinal cord injury,” J. Spinal Cord Med., vol. 41, no. 3, pp. 361–366, May 2018.
[6]   Z. Li, D. Guiraud, D. Andreu, C. Fattal, A. Gelis, and M. Hayashibe, “A hybrid functional electrical stimulation for real-time estimation of joint torque and closed-loop control of muscle activation,” Eur. J. Transl. Myol., vol. 26, no. 3, Jun. 2016.
[7]   M. Farokhzadi, A. Maleki, A. Fallah, and S. Rashidi, “Online estimation of elbow joint angle using upper arm acceleration: A movement partitioning approach,” . J. Biomed. Phys. Eng, vol. 7, 2017.
[8]   R. Raj and K. S. Sivanandan, “Estimation of Elbow Joint Angle from Time Domain Features of SEMG Signals Using Fuzzy Logic for Prosthtic Control,” Int. J. Curr. Eng. Technol., vol. 5, no. 3, pp. 2078–2081, 2015.
[9]   C. P. E. Giuffrida P.J., “Functional Restoration of Elbow Extension After Spinal- Cord Injury Using a Neural Network-Based Synergistic FES Controller,” IEEE Trans. neural Syst. Rehabil. Eng., vol. 13, 2005.
[10]R. R. Kaliki, R. Davoodi, and G. E. Loeb, “Evaluation of a Noninvasive Command Scheme for Upper-Limb Prostheses in a Virtual Reality Reach and Grasp Task,” IEEE Trans. Biomed. Eng., vol. 60, no. 3, 2013.
[11]M. A. Toosi, A. Maleki, and A. Fallah, “Estimation and anticipation of elbow joint angle from shoulder data during planar movements,” in The 2nd International Conference on Control, Instrumentation and Automation, 2011, pp. 1222–1225.
[12]L. Iuppariello, “Modelling and Performance Assessment of Human Reaching Movements for Disease Classification,” Università degli Studi di Napoli Federico II, 2015.
[13]D. Elliott, S. Hansen, L. E. M. Grierson, J. Lyons, S. J. Bennett, and S. J. Hayes, “Goal-Directed Aiming: Two Components but Multiple Processes,” Psychol. Bull., vol. 136, no. 6, pp. 1023–1044, 2010.
[14]A. Fishbach, S. A. Roy, C. Bastianen, L. E. Miller, and J. C. Houk, “Kinematic properties of on-line error corrections in the monkey,” Exp. Brain Res., vol. 164, no. 4, pp. 442–457, 2005.
[15]E. Burdet and T. E. Milner, “Quantization of human motions and learning of accurate movements,” Biol. Cybern., vol. 78, no. 4, pp. 307–318, May 1998.
[16]A. Frisoli, C. Loconsole, R. Bartalucci, and M. Bergamasco, “A new bounded jerk on-line trajectory planning for mimicking human movements in robot-aided neurorehabilitation,” Rob. Auton. Syst., vol. 61, no. 4, pp. 404–415, Apr. 2013.
[17]J. M. Todorov E, “Optimal feedback control as a theory of motor coordination,” Nat Neurosci, no. 5, pp. 1226–1235, 2002.
[18]W. D. H arris CM, “Signal dependent noise determines motor planning,” N ature, pp. 780–784, 1998.
[19]P. Huang, Y. Xu, and B. Liang, “Global minimum-jerk trajectory planning of space manipulator Global Minimum-Jerk Trajectory Planning of Space Manipulator,” Int. J. Control Autom. Syst., vol. 47, no. 1, 2016.
[20]ع. مالکی, علی, فلاح, “بررسی استفاده از سینرژی کینماتیک به منظور کنترل تحریک الکتریکی عملکردی حرکت رساندن دست در صفحه,” نشریه علمی پژوهشی امیرکبیر, vol. 40, no. 1, pp. 9–9, 1388.
[21]R. S. Farokhzadi M, Maleki A, Fallah A, “Online Estimation of Elbow Joint Angle Using Upper Arm Acceleration: A Movement Partitioning Approach.,” J Biomed Phys Eng, vol. 7, no. 3, pp. 305–314, 2017.
[22]T. Flash and N. Hogan, “The coordination of arm movements: an experimentally confirmed mathematical model.,” J. Neurosci., vol. 5, no. 7, pp. 1688–1703, 1985.
[23]R. Plamondon, A. M. Alimi, P. Yergeau, and F. Leclerc, “Modelling velocity profiles of rapid movements: a comparative study,” Biol. Cybern., vol. 69, no. 2, pp. 119–128, 1993.
[24]J. Y. Liao and R. F. Kirsch, “Characterizing and Predicting Submovements during Human Three-Dimensional Arm Reaches,” vol. 9, no. 7, 2014.
[25]S. S. Naghibi, A. Maleki, and A. Fallah, “A modified method of submovement decomposition based on velocity profile and endpoint position,” in 24th Iranian Conference of Biomedical Engineering (ICBME), 2017, pp. 1–4.
[26]V. S. ; J. A. ; D. I. ; B. Raghavan, “Finding a ‘Kneedle’ in a Haystack: Detecting Knee Points in System Behavior,” in 31st International Conference on Distributed Computing Systems Workshops, 2011.