Exploring the Effect of Foot Position on Performance of a Modular and Hierarchical Movement Planner in Planning the Sit-to-Stand Transfer

Authors

Abstract

Many researchers in the science of human behavior explore the role and function of the central nervous system (CNS) in planning and controlling of human movements. To this end, researchers have presented several different models. In present research a computer simulation of CNS's performance in designing the Sit-to-Stand transfer, which has been recently presented, is developed. This motion simulator is a modular and hierarchical movement planner (MHMP), based on decomposition hypothesis. In this paper the effect of foot position, as an environmental condition, is explored. The performance of the MHMP is evaluated with experimental captured motion. To this end, sit to stand motion is captured for five different foot position. Among these five motions, three motions are used to train the MHMP and the remaining motions are used for evaluating its performance. Results show that the maximum error of ankle, knee and hip angles are 0.122, 0.069 and 0.092 (radian) respectively. The maximum allowed error, suggested in researches, is 0.17(radian). The results show that the MHMP has a good performance in planning the motion phases under this condition.

Keywords


[1] Guyton AC, Hall JE (2006) Textbook of medical physiology  elsevier saunders, philadelphia.
[2] Flash T, Hogan N (1985) The coordinate of movement: an experimentally confirmed mathematical model. J Neurosci 5(7): 1688-1703.
[3] Uno YMK, Suzuki R (1989) Formation and control of optimal trajectory in human multijoint arm movement. Biol Cybern 61(2): 89-101.
[4] Pandi MG, Garner BA (1995) Optimal control of non ballestic muscular movement: A constraint based performance criterion for rising from A chair. J Biomech Eng 117(1): 15-26.
[5] Bahrami F, Emadi-Andani M, Jabedar P (1999) Prediction of the joint trajectories during rising from a chair applying two methods for parameterization of the search-space. Proc. 7th Iranian Conference on Electrical Engineering. Tehran, Iran: 9-16
[6] Cisek P (2005) Neural representations of motor plans, desired trajectories and controlled objects. Cogn Process 6(1): 15-24.
[7] Emadi-Andani M, Bahrami F, Yazdanpanah MJ, Patla A (2004) Movement prediction using a MLP without an internal feedback. Proc. IEEE International Conference on System Man and Cybernatics. Netherlands: 5975-5979.
[8] Kang-Lee JYN (2008) Knee joint moment estimate using neural network system identification in sit-to-stand movement. Proc. International Conference on Control Automation and System. Korea: 14-17.
[9] Qu X, Nussbaum MA (2009) Simulating Human Lifting Motion Using Fuzzy-Logic Control. IEEE T Syst Man Cy A 39(1): 109-118.
[10] Emadi-Andani M, Bahrami F, Maralani P, Ijspeert AJ (2009) MODEM: A multi-agent hierarchical structure to model the human motor control system. Biol Cybern 101(5-6): 361-377.
[11] Haruno M, Wolpert DM, Kawato M (2003) Hierarchical MOSAIC For movement generation. Proc. International Congress Series: 575-590.
[12] Imamizu H, Kuroda T, Miyauchi S, Yoshika T, Kawato M (2003) Modular organization of internal models of tools in the human cerebellum. J Neurosci 100(9): 5461-5466.
[13] Mussa-Ivaldi FA (1999) Modular feature of motor control and learning. Curr Opin Neurobiol 9(6): 713-717.
[14] Sadeghi M, Emadi-Andani M, Parnianpour M, Fattah A (2013) A bio-inspired modular hierarchical structure to plan the sit-to-stand transfer under varying environmental conditions. Neurocomputing 118(1): 311-321.
[15] Riley PO, Schenkman ML, Mann RW, Hodge WA (1991) Mechanics of a constrained chair-rise. J Biomech 24(1): 77-85.
[16] Boonstra M (2010) The sit-to-stand movement:A clinical evaluation tool for knee and hip arthroplasty patients. PhD Thesis, Radboud University.
[17] Kawagoe S, Tajima N, Chosa E (2000) Biomechanical analysis of effects of foot placement with varying chair height on the motion of standing up. J Orthop Sci 5 (2): 124-133.
[18] Scholz JP, Reisman D, Schoner G (2001) Effects of varying task constraints on solution to joint coordination in a Sit-To-Stand task. Exp Brain Res 141(4): 485-500.
[19] Nakano E, Imamizu H, Osu R, Uno Y, Gomi H, Yoshioka T, Kawato M (1999) Quantitative examinations of internal representation for arm trajectory planning: Minimum commanded torque change model. ‎J Neurophysiol 81(5): 497-509.
[20] Emadi-Andani M, Bahrami F (2012) COMAP: A new computational interpretation of human movement planning level based on coordinated minimum angle jerk policies and 6 universal movement elements. Hum Movement Sci 31(5): 1037-1055.
[21] Emadi-Andani M, Bahrami F, Maralani Pj (2009) AMA-MOSAICI: An automatic module assigning hierarchical structure to control human motion based on movement decomposition. Neurocomputing 72(10-12): 2310-2318.
[22] Takagi T, Sugeno M (1985) Fuzzy identification of system and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1): 116-132.
[23] Askarishahi M, Hadian Jazi S, Jamshidi N, FreshtehNejad N (2015) Evaluation ofƒmodular and hierarchical movement planner under combination of different conditions in planning the Sit-to-Stand transfer. Modares Mech Eng 15(9): 105-115.  (In Persian)
[24] Sadeghi M (2012) Movement planning of Sit-to-Stand transfer based on decomposing the motion into its corresponding subtasks. M.Sc Thesis, Isfahan University of Technology.
[25] Galli M, Cimolin V, Crivellini M, Campanini I, (2008) Quantitative analysis of sit to stand movement: Experimental set-up definition and application to healthy and hemiplegic adults. Gait Posture 28(1): 80-85.
[26] Perez MA (2005) Prediction of whole body lifting kinematics using artificial neural network. Ph.D Thesis, Virginia Polytechnic Institute and State University.