• Logo
  • SBMUJournals

Modeling Multiple Sclerosis at Different Levels Using Reinforcement Learning

Samira Gharehali, Fereidoun Nowshiravan Rahatabad, Zahra Einalou




Background: Multiple sclerosis (MS) represents one of the most common disorders of the central nervous system, which leads to the dysfunction of different body systems and generates a myriad of problems for the affected individuals. Given the progressive nature of this disease, it can divide into several levels. The progression rate of the disease at each stage is essential for specialists, as it can help them to adopt appropriate therapeutic measures.
Methods: One of the methods used in many MS neurological treatments is Expanded Disability Status Scale (EDSS), which allows physicians to give an estimate of the severity of the disease to patients, learn about the stage of the patient’s disease and prescribe appropriate medicines accordingly. Given the importance and impact of this disease on the quality of life of patients, researchers look for inexpensive and simple models with minimum side effects for examining different levels of MS and providing treatment solutions.
Results: In this study, patients were asked to stand on a force plate. Then, the time series of the center of pressure and body oscillations of patients at various levels were recorded using a motion analyzer device, and a closed loop control system was proposed using the reverse pendulum (representing human body) and reinforcement learning.
Conclusion: Based on the feedback received from the environment, the necessary rules for maintaining the balance of pendulum obtained, and, by observing the ankle torque at the output, a model presented that could examine different levels of MS.


Multiple sclerosis; Modeling; Reinforcement learning; Expanded Disability Status Scale.


Ghezzi A, Comi G, Federico A. Chronic cerebro-spinal venous insufficiency (CCSVI) and multiple sclerosis. Neurol Sci. 2011;32(1):17-21. doi: 10.1007/s10072-010-0458-3.

Einalou Z, Maghooli K, Setarehdan SK, Akin A. Functional near infrared spectroscopy to investigation of functional connectivity in schizophrenia using partial correlation. Universal Journal of Biomedical Engineering. 2014;2(1):5-8. doi: 10.13189/ujbe.2014.020102.

Einalou Z, Maghooli K, Setarehdan SK, Akin A. Functional near infrared spectroscopy for functional connectivity during Stroop test via mutual information. Adv Biores. 2015;6(1):62- 7.

Einalou Z, Maghooli K, Setarehdan SK, Akin A. Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals. Optik - International Journal for Light and Electron Optics. 2016;127(6):3271-5. doi: 10.1016/j. ijleo.2015.12.090.

Barahimi S, Einalou Z, Dadgostar M. Studies on schizophrenia and depressive diseases based on functional near-infrared spectroscopy. Biomed Eng Appl Basis Commun. 2018;30(4):1830002. doi: 10.4015/s101623721830002x.

Dadgostar M, Setarehdan SK, Shahzadi S, Akin A. Classification of schizophrenia using SVM via fNIRS. Biomed Eng Appl Basis Commun. 2018;30(02):1850008. doi: 10.4015/s1016237218500084.

Kurtzke JF. Origin of DSS: to present the plan. Mult Scler. 2007;13(1):120-3. doi: 10.1177/1352458506071584.

Kurtzke JF. Historical and clinical perspectives of the expanded disability status scale. Neuroepidemiology. 2008;31(1):1-9. doi: 10.1159/000136645.

Krishnan V, Kanekar N, Aruin AS. Feedforward postural control in individuals with multiple sclerosis during load release. Gait Posture. 2012;36(2):225-30. doi: 10.1016/j. gaitpost.2012.02.022.

Bonnet V, Fraisse P, Ramdani N, Lagarde J, Ramdani S, Bardy B. Modeling postural coordination dynamics using a closed-loop controller. In: Humanoid Robots, 2008. 8th IEEE-RAS International Conference on 2008 (pp. 61-66). IEEE.

Patton JL. Global modeling of adaptive, dynamic balance control [dissertation]. Evanston, IL: Northwestern University; 1998.

Paillard T, Noe F. Techniques and Methods for Testing the Postural Function in Healthy and Pathological Subjects. Biomed Res Int. 2015;2015:891390. doi: 10.1155/2015/891390.

Ishida A, Miyazaki S. Maximum likelihood identification of a posture control system. IEEE Trans Biomed Eng. 1987;34(1):1- 5. doi: 10.1109/TBME.1987.326023.

Hof AL, Gazendam MG, Sinke WE. The condition for dynamic stability. J Biomech. 2005;38(1):1-8. doi: 10.1016/j. jbiomech.2004.03.025.

Sutton RS, Barto AG. Reinforcement learning: An introduction. Cambridge, MA: MIT press; 1998.

Chagdes JR, Rietdyk S, Haddad JM, Zelaznik HN, Cinelli ME, Denomme LT, et al. Limit cycle oscillations in standing human posture. J Biomech. 2016;49(7):1170-9. doi: 10.1016/j. jbiomech.2016.03.005.

Boes MK, Hsiao-Wecksler ET, Motl RW, Sosnoff JJ. Postural control model of spasticity in persons with multiple sclerosis [dissertation]. Urbana, IL; University of Illinois; 2011.

Corradini ML, Fioretti S, Leo T, Piperno R. Early recognition of postural disorders in multiple sclerosis through movement analysis: a modeling study. IEEE Trans Biomed Eng. 1997;44(11):1029-38. doi: 10.1109/10.641330.

Chagdes JR, Rietdyk S, Jeffrey MH, Howard NZ, Raman A. Dynamic stability of a human standing on a balance board. J Biomech. 2013;46(15):2593-602. doi: 10.1016/j. jbiomech.2013.08.012.


  • There are currently no refbacks.