A LINEAR MODEL-BASED SIMULATION TOOL FOR ESTIMATING NUMBER OF TRIALS NEEDED FOR UPPER LIMB STROKE RECOVERY IN A GIVEN REHABILITATION SESSION
DOI:
https://doi.org/10.4314/njt.v42i3.10Keywords:
Performance stability, Linear models, Iterative learning algorithm, Trials, Estimation, Stroke, Kinematic accuracyAbstract
Traditional methods for assessing upper-limb functional outcomes in stroke patients often fail to estimate the number of trials required to achieve performance stability of a chosen kinematic metric. Limited non-model-based studies have attempted to tackle this issue. To bridge this gap, this study utilized an iterative learning algorithm (ILA) in MATLAB, employing linear models to represent the muscle dynamics and forearm extension of impaired patients. The reference task space trajectory was set as a straight-line point-point trajectory within a range of 0 - 0.2828m. By using the root mean square error (RMSE) as a metric for evaluating kinematic accuracy, a maximum kinematic deviation error of 0.01m was imposed with respect to the trajectory by the (ILA). Results indicate that over 16 trials, performance stability was obtained with improvement in deviation error from 0.0168m in the first trial to 0.0060 at sixteen trials. The result obtained is in line with similar non-model studies and our findings inform the potential of ILAs with linear models for estimation of trial numbers required to attain performance stability of a selected kinematic metric (i.e., kinematic accuracy).
References
Markus, H. “Stroke: causes and clinical features”, Medicine, Vol. 36, Number 11, 2008, pp 586-91.
Muir, K. W. “Stroke”, Medicine, Vol. 41, Number 3, 2013, pp 169–74.
Jaberinezhad, M., Farhoudi, M., Nejadghaderi, S. A., Alizadeh, M., Sullman, M. J. M., Carson-Chahhoud, K., Collins, G. S., and Safiri, S. “The burden of stroke and its attributable risk factors in the Middle East and North Africa region, 1990-2019”. Sci Rep, Vol. 12, Number 1, 2022, pp 2700.
Grefkes, C., and Fink, G. R. “Recovery from stroke: current concepts and future perspectives”, Neurol Res Pract, Vol 2, Number 17, 2020, doi: 10.1186/s42466-020-00060-6.
Alawieh, A., Zhao, J., and Feng, W. “Factors affecting post-stroke motor recovery: Implications on neurotherapy after brain injury”, Behav Brain Res, Number 340, 2018, pp 94-101. doi: 10.1016/j.bbr.2016.08.029.
Fadhilah, H., and Permanasari V. Y. “Economic Burden Bore by Patients and Families because of stroke: Policy Assessment”, Journal of Indonesian Health Policy and Administration, Vol. 5, Number 3, 2020, pp 91-95.
Liu, Y., Hong, Y., and Ji, L. “Dynamic Analysis of the Abnormal Isometric Strength Movement Pattern between Shoulder and Elbow Joint in Patients with Hemiplegia”, Journal of healthcare engineering, Vol. 2018, Number 1817485, 2018, pp 7.
Zavoreo, I., Bašić-Kes, V. A., and Demarin, V. “Stroke and neuroplasticity”, Periodicum Biologorum. Vol. 114, Number 3, 2012, pp 393-6.
Stinear, C. M., Smith, M. C., and Byblow, W. D. “Prediction Tools for Stroke Rehabilitation”, Stroke. Vol. 50, Number 11, 2019, pp 3314-3322. doi: 10.1161/STROKEAHA.119.025696
Kiaer, C., Lundquist, C. B., and Brunner, I. “Knowledge and application of upper limb prediction models and attitude toward prognosis among physiotherapists and occupational therapists in the clinical stroke setting”, Top Stroke Rehabil. Vol. 28,Number 2, 2021, pp 135-141. doi: 10.1080/1074935 7.2020.1783915
Stinear, C. M., Barber, P. A., Petoe, M., Anwar, S., and Byblow, W. D. “The PREP algorithm predicts potential for upper limb recovery after stroke”, Brain. Vol. 135, Number 8, 2012 pp 2527-35. doi: 10.1093/brain/aws146.
Kwah, L. K., Harvey, L. A., Diong, J., and Herbert, R. D. “Models containing age and NIHSS predict recoveryof ambulation and upper limb function six months after stroke: anobservational study”, J Physiother. Vol. 59, Number 3, 2013, pp 189-97. doi:10.1016/S1 8369553(13)70183-8.
Stinear, C. M., Byblow, W. D., Ackerley, S. J., Smith, M. C., Borges, V. M., and Barber, P. A. “PREP2: A biomarker-based algorithm for predicting upper limb function after stroke”, Ann Clin Transl Neurol. Vol. 4, Number 11, 2017, pp 811-820. doi: 10.1002/acn3.488.M klm
Tozlu, C., Edwards, D., Boes, A., Labar, D., Tsagaris, K. Z., Silverstein, J., Pepper Lane, H., Sabuncu, M. R., Liu, C., and Kuceyeski, A. “Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke”, Neurorehabil Neural Repair. Vol. 34, Number 5, 2020, pp 428-439. doi: 10.1177/1545968320909796.
Selles, R. W., Andrinopoulou, E. R., Nijland, R. H., Van der Vliet, R., Slaman, J., van Wegen, E. E., Rizopoulos, D., Ribbers, G. M., Meskers, C. G., and Kwakkel, G. “Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: the next step”, J Neurol Neurosurg Psychiatry. Vol. 92, Number 6 , 2021, pp 574–81. doi: 10.1136/jnnp-2020-324637
Alt Murphy, M., Al-Shallawi, A., Sunnerhagen, K. S., and Pandyan, A. “Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study”, Sci Rep. Vol. 12, Number 22053, 2022.
Houda, B., Nahla, K., and Safya, B. “Musculoskeletal Modeling of Elbow Joint under Functional Electrical Stimulation”, International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 2019, pp. 307-310.
Freeman, C. T., Hughes, A. M., Burridge, J. H., Chappell, P. H., Lewin, P. L., and Rogers, E. “A robotic workstation for stroke rehabilitation of the upper extremity using FES”, Medical engineering and physics. Vol. 31, Number 3, 2009, pp 364-73.
Ayodele, K. P., Akinniyi, O. T., Oluwatope, A. O., Jubril, A. M., Ogundele, A. O., and Komolafe, M. A. “A Simulator for Testing Planar Upper Extremity Rehabilitation Robot Control Algorithms”, Nigerian Journal of Technology. Vol. 40, Number 1, 2021, pp 115-128.
Oña, E. D., Garcia-Haro, J. M., Jardón, A., and Balaguer, C. “Robotics in health care: Perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs”, Applied sciences. Vol. 9, Number 13, 2019, pp 2586.
Li, S. “Spasticity, motor recovery, and neural plasticity after stroke”, Frontiers in neurology. Vol. 8, Number 120, 2017.
T. Herrgårdh et al., “Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios”, NeuroImage: Clinical, vol. 31, p. 102694, Jan. 2021, doi: 10.1016/j.nicl.2021.10 2694.
Hochstenbach-Waelen and Seelen, H. H. “Embracing change: practical and theoretical considerations for successful implementation of technology assisting upper limb training in stroke”, Journal of Neuroengineering and Rehabilitation, vol. 9, no. 1, p. 52, Jan. 2012, doi: 10.1186/1743-0003-9-52.
Frykberg, G. E., Grip, H., and Murphy, M. A. “How many trials are needed in kinematic analysis of reach-to-grasp?—A study of the drinking task in persons with stroke and non-disabled controls”, Journal of Neuroengineeri-ng and Rehabilitation, vol. 18, no. 1, Jun. 2021, doi: 10.1186/s12984-021-00895-3.
Chen, Y. P., Garcia-Vergara, S., and Howard, A. M. “Number of trials necessary to achieve performance stability in a reaching kinematics movement analysis game”, Journal of Hand Therapy, Jul. 2020, doi: 10.1016/j.jht.2019.0 4.001.
Schwarz, A., Kanzler, C. M., Lambercy, O., Luft, A. R., and J. M. Veerbeek, “Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke,” Stroke, vol. 50, no. 3, pp. 718–727, Mar. 2019, doi: 10.1161/strokeaha.118.023531.
Geng, Y., Chen, Z., Zhao, Y., Cheung, V. C., Li, G. “Applying muscle synergy analysis to forearm high-density electromyography of healthy people”, Frontiers in Neuroscience. Vol. 16, 2022. https://doi.org/10.3389/fnins.2 022.1067925
Freeman, C., Hughes, A. M., Burridge, J., Chappell, P. H., Lewin, P. L., and Rogers, E. “Iterative learning control of FES applied to the upper extremity for rehabilitation”, Control Engineering Practice, vol. 17, no. 3, pp. 368–381, Mar. 2009, doi: 10.1016/j.conengprac.200 8.08.003.
Le, F., Markovsky, I., Freeman, C. T., and Rogers, E. “Identification of the Dynamics of Human Arms After Stroke”, Proceedings of the 23rd IAR Workshop on Advanced Control and Diagnosis, 2008.
Durfee, W. K., and Palmer, K. I. “Estimation of force-activation, force-length, and force velocity properties in isolated, electrically stimulated muscle”, IEEE Transactions on Biomedical Engineering. Vol. 41, Number 3, 1994, pp 205-16.
Le, F., Markovsky, I., Freeman, C. T., and Rogers, E. “Identification of electrically stimulated muscle models of stroke patients”, Control Engineering Practice. Vol 18, Number 4, 2010, pp 396–407.
Fadali, M. S., and Visioli, A. “Digital control engineering”, Analysis and design. Elsevier, Waltham, 2013, pp. 157-165.
Delchev, K. “Iterative learning control for nonlinear systems: A bounded-error algorithm”, Asian Journal of Control, Vol. 15, Number 2, 2013, pp 453–460.
Bristow, D., Tharayil, M., and Alleyne, A. “A Learning-Based Method for High-Performance Tracking Control”, Ieee Control Systems Magazine, Vol. 1066, Number 033X, 2006, pp 96–114.
Ahn, H., Chen, Y., and Moore, K. L. “Iterative learning control: brief survey an categorization”, IEEE Control Systems, Vol. 37, Number 435, 2004, pp 1–54.
Ramli, N. N. N., Asokan, A., Mayakrishnan, D., and Annamalai, H. “Exploring Stroke Rehabilitation in Malaysia: Are Robots Better than Humans for Stroke Recuperation?”, Malays J Med Sci, vol. 28, no. 4, pp. 14–23, Aug. 2021, doi: 10.21315/mjms2021.28.4.3.
Keeling, B., Piitz, M. A., Semrau, J. A., Hill, M. D., Scott, S., and Dukelow, S. P. “Robot enhanced stroke therapy optimizes rehabilitat-ion (RESTORE): a pilot study”, Journal of Neuroengineering and Rehabilitation, vol. 18, no. 1, Jan. 2021, doi: 10.1186/s12984-021-00804-8.
Xu, W., Chu, B., and Rogers, E. “Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue”, Control Engineering Practice, vol. 31, pp. 63–72, Oct. 2014, doi: 10.1016/j.conengpr ac.2014.05.009
Bessler, J., et al., “Safety Assessment of Rehabilitation Robots: A Review Identifying Safety Skills and Current Knowledge Gaps”, Frontiers in Robotics and AI, vol. 8, Mar. 2021, doi: 10.3389/frobt.2021.602878.
Desplenter, T., and Trejos, A. L. “A Control Software Framework for Wearable Mechatro-nic Devices”, Journal of Intelligent & Robotic Systems, vol. 99, no. 3–4, pp. 757–771, Jan. 2020, doi: 10.1007/s10846-019-01144-5.
Clark, B., Whitall, J., Kwakkel, G., Mehrholz, J., Ewings, S., and Burridge, J. “Time spent in rehabilitation and effect on measures of activity after stroke”, The Cochrane Library, Mar. 2017, doi: 10.1002/14651858.cd012612.
Kwakkel, G., et al., “Standardized measure-ment of quality of upper limb movement after stroke: Consensus-based core recommendat-ions from the Second Stroke Recovery and Rehabilitation Roundtable”, International Journal of Stroke, vol. 14, no. 8, pp. 783–791, Sep. 2019, doi: 10.1177/1747493019873519.
Taragna, M., Cannizzaro, D., and Votano, S. “Vibration compensation for robotic manipulators by iterative learning control”, POLITECNICO DI TORINO. 2018.
Moore, K. L., Chen, Y., and Ahn, H. S. “Iterative learning control: A tutorial and big picture view”, In Proceedings of the IEEE Conference on Decision and Control. Vol. 45, 2006, pp 2352-2357.
Ardakani, M. G., Khong, S. Z., and Bernhardsson, B. “On the convergence of iterative learning control”, Automatica. Vol. 78, 2017, pp 266 – 273.
Freeman, C., Hughes, A. M., Burridge, J., Chappell, P. H., Lewin, P., and Rogers, E. “A model of the upper extremity using FES for stroke rehabilitation”, Journal of Biomechanic-al Engineering, vol. 131, no. 3, Jan. 2009, doi: 10.1115/1.3005332.
Subramanian, S., Baniña, M. C., Turolla, A., and Levin, M. F. “Reaching performance scale for stroke – Test retest reliability, measurement error, concurrent and discriminant validity”, Pm and R, vol. 14, no. 3, pp. 337–347, Apr. 2021, doi: 10.1002/pmrj.12584
Blinch, J., Kim, Y., and Chua, R. “Trajectory analysis of discrete goal-directed pointing movements: How many trials are needed for reliable data?”, Behavior Research Methods, vol. 50, no. 5, pp. 2162–2172, Dec. 2017, doi: 10.3758/s13428-017-0983-6.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Nigerian Journal of Technology

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The contents of the articles are the sole opinion of the author(s) and not of NIJOTECH.
NIJOTECH allows open access for distribution of the published articles in any media so long as whole (not part) of articles are distributed.
A copyright and statement of originality documents will need to be filled out clearly and signed prior to publication of an accepted article. The Copyright form can be downloaded from http://nijotech.com/downloads/COPYRIGHT%20FORM.pdf while the Statement of Originality is in http://nijotech.com/downloads/Statement%20of%20Originality.pdf
For articles that were developed from funded research, a clear acknowledgement of such support should be mentioned in the article with relevant references. Authors are expected to provide complete information on the sponsorship and intellectual property rights of the article together with all exceptions.
It is forbidden to publish the same research report in more than one journal.