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Stroke survivors exhibit stronger lower extremity synergies in more challenging walking conditions

Abstract

The aim of this study was to examine how kinematic synergies are utilised as compensatory movements to stabilise foot positions under different walking task constraints in people with stroke. Ten (Males = 6, Females = 4) hemiplegic chronic stroke survivors volunteered to participate in this study, recruited from a rehabilitation centre. They completed a consent form and participated in treadmill walking tasks; flat, uphill, and crossing over a moving obstacle. The uncontrolled manifold method was used to quantify kinematic synergies in the paretic and non-paretic legs during their swing phase. The results of this study showed the strength of synergies was significantly greater in the obstacle task than in the uphill walking tasks at mid and terminal swing phases. In conclusion, the results suggest that walking in the challenging situations caused people with stroke to control step stability with greater compensation between lower extremity joints. Participants adapted to the increased challenge by increasing the amount of ‘good variability’, which could be a strategy to reduce the risks of falling.

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References

  1. Balasubramanian CK, Neptune RR, Kautz SA (2009) Variability in spatiotemporal step characteristics and its relationship to walking performance post-stroke. Gait Posture 29:408–414

    Article  PubMed  Google Scholar 

  2. Bernstein N (1967) The coordination and regulation of movements. Pergamon Press, London

    Google Scholar 

  3. Carr JH, Shepherd RB, Nordholm L, Lynne D (1985) Investigation of a new motor assessment scale for stroke patients. Phys Ther 65:175–180

    Article  CAS  PubMed  Google Scholar 

  4. Dean JC, Kautz SA (2015) Foot placement control and gait instability among people with stroke. J Rehabil Res Dev 52:577–590

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gelfand IM, Latash ML (1998) On the problem of adequate language in movement science. Mot Control 2:306–313

    Article  CAS  Google Scholar 

  6. Hutin E, Pradon D, Barbier F, Gracies JM, Bussel B, Roche N (2011) Lower limb coordination patterns in hemiparetic gait: factors of knee flexion impairment. Clin Biomech 26:304–311

    Article  Google Scholar 

  7. Jackson AB, Carnel CT, Read MS, Boninger ML, Schmeler MR, Williams SR, Donovan WH (2008) Outcome measures for gait and ambulation in the spinal cord injury population. J Spinal Cord Med 31:487–499

    Article  PubMed  PubMed Central  Google Scholar 

  8. Janshen L, Santuz A, Ekizos A, Arampatzis A (2017) Modular control during incline and level walking in humans. J Exp Biol 220:807–813

    PubMed  Google Scholar 

  9. Kao P, Srivastava S (2018) Mediolateral footpath stabilization during walking in people following stroke. PloS One 13:e0208120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Krishnan V, Rosneblatt NJ, Latash ML, Grabiner MD (2013) The effects of age on stabilisation of the mediolateral trajectory of the swing foot. Gait Posture 38:923–928

    Article  PubMed  Google Scholar 

  11. Latash ML, Scholz JP, Schoner G (2007) Toward a new theory of motor synergies. Mot Control 11:276–308

    Article  Google Scholar 

  12. Liao Y, Yang Y, Wu Y, Wan R (2014) Factors influencing obstacle crossing performance in patients with Parkinson’s Disease. PLoS One 9:e84245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lowrey CR, Watson A, Vallis LA (2007) Age-related changes in avoidance strategies when negotiating single and multiple obstacles. Exp Brain Res 182:289–299

    Article  PubMed  Google Scholar 

  14. Lu TW, Yen HC, Chen HL, Hsu WC, Chen SC, Hong SW (2010) Symmetrical kinematic changes in highly functioning older patients post-stroke during obstacle-crossing. Gait Posture 31:511–516

    Article  PubMed  Google Scholar 

  15. MacLellan MJ, Richards CL, Fung J, McFadyen BJ (2015) Comparison of kinetic strategies for avoidance of an obstacle with either the affected or unaffected as leading limb in persons post stroke. Gait Posture 42:329–334

    Article  PubMed  Google Scholar 

  16. Manaf H, Justine M, Goh HT (2015) Effects of attentional loadings on gait performance before turning in stroke survivors. PM&R 7:1159–1166

    Article  Google Scholar 

  17. Mattos D, Latash ML, Park E, Kuhl J, Scholz JP (2011) Unpredictable elbow joint perturbation during reaching results in multijoint motor equivalence. J Neurophysiol 106:1424–1436

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Moore S, Schurr K, Wales A, Moseley A, Herbert R (1993) Observation and analysis of hemiplegic gait: swing phase. Aus J Physiother 39:271–278

    Article  CAS  Google Scholar 

  19. Moseley A, Wales A, Herbert R, Schurr K, Moore S (1993) Observation and analysis of hemiplegic gait: stance phase. Aus J Physiother 39:259–267

    Article  CAS  Google Scholar 

  20. Newell KM, Broderick MP, Deutsch KM, Slifkin AB (2003) Task goals and change in dynamical degrees of freedom with motor learning. J Exp Psychol Hum Percept Perform 29:379–387

    Article  PubMed  Google Scholar 

  21. Novak AC, Deshpande N (2014) Effects of aging on whole body and segmental control while obstacle crossing under impaired sensory conditions. Hum Mov Sci 35:121–130

    Article  PubMed  Google Scholar 

  22. Olney SJ, Eng J (2011) Gait. In: Levangie PK, Norkin CC (eds) Joint structure and function, 5th edn. Philadelphia, F. A. Davis Company, pp 524–569

    Google Scholar 

  23. Pandy MG, Lin YC, Kim HJ (2010) Muscle coordination of mediolateral balance in normal walking. J Biomech 43:2055–2064

    Article  PubMed  Google Scholar 

  24. Papi E, Rowe PJ, Pomeroy VM (2015) Analysis of gait within the uncontrolled manifold hypothesis: stabilisation of the centre of mass during gait. J Biomech 48:324–331

    Article  PubMed  Google Scholar 

  25. Perry J, Burnfield JM (2010) Larger contributions of the hip and knee extensors as well as the stabilizing knee muscles ST and BFL in Syn 1. NJ Slack Inc, Thorofare

    Google Scholar 

  26. Plummer-D’Amato P, Altmann LJP, Saracino D, Fox E, Behrman AL, Marsiske M (2008) Interactions between cognitive tasks and gait after stroke: a dual task study. Gait Posture 27:683–688

    Article  PubMed  Google Scholar 

  27. Plummer-D’Amato P, Kyvelidou A, Sternad D, Najafi B, Villalobos RM, Zurakowsk D (2012) Training dual-task walking in community-dwelling adults within 1 year of stroke: a protocol for a single-blind randomized controlled trial. BMC Neurol 12:129–137

    Article  PubMed  PubMed Central  Google Scholar 

  28. Qu X (2012) Uncontrolled manifold analysis of gait variability: effects of load carriage and fatigue. Gait Posture 36:325–329

    Article  PubMed  Google Scholar 

  29. Rankin BL, Buffo SK, Dean JC (2014) A neuromechanical strategy for mediolateral foot placement in walking humans. J Neurophysiol 112:374–383

    Article  PubMed  PubMed Central  Google Scholar 

  30. Rosenblatt NJ, Hurt CP, Latash ML, Grabiner MD (2014) An apparent contradiction: increasing variability to achieve greater precision? Exp Brain Res 232:403–413

    Article  PubMed  Google Scholar 

  31. Rosenblatt NJ, Latash ML, Hurt CP, Grabiner MD (2015) Challenging gait leads to stronger lower-limb kinematics synergies: the effects of walking within a more narrow pathway. Neurosci Lett 600:110–114

    Article  CAS  PubMed  Google Scholar 

  32. Scholz JP, Schöner G (1999) The uncontrolled manifold concept: identifying control variables for a functional task. Exp Brain Res 126:289–306

    Article  CAS  PubMed  Google Scholar 

  33. Shamay S, Hui-Chan CW (2005) The timed up and go test: its reliability and association with lower-limb impairments and locomotor capacities in people with chronic stroke. Arch Phys Med Rehabil 86:1641–1647

    Article  Google Scholar 

  34. Stanhope VA, Knarr BA, Reisman DS, Higginson JS (2014) Frontal plane compensatory strategies associated with self-selected walking speed in individuals post-stroke. Clin Biomech 29:518–522

    Article  Google Scholar 

  35. Tukoda K, Anan M, Takahashi M, Sawada T, Tanimoto K, Kito N, Shunkoda K (2018) Biomechanical mechanism of lateral trunk lean gait for knee osteoarthritis patients. J Biomech 66:10–17

    Article  Google Scholar 

  36. Van Emmerik REA, Hamill J, McDermott WJ (2005) Variability and coordinative function in human gait. Quest 57:102–123

    Article  Google Scholar 

  37. Winter DA (1987) The biomechanics and motor control of human gait. University of Waterloo Press, Waterloo

    Google Scholar 

  38. Wong AM, Pei YC, Hong WH, Chung CY, Lau YC, Chen CP (2004) Foot contact pattern analysis in hemiplegic stroke patients: an implication for neurologic status determination. Arch Phys Med Rehabil 85:1625–1630

    Article  PubMed  Google Scholar 

  39. Wu Y, Pazin N, Zatsiorsky VM, Latash ML (2012) Practicing elements versus practicing coordination: changes in the structure of variance. J Mot Behav 44:471–478

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yang JF, Scholz JP (2005) Learning a throwing task is associated with differential changes in the use of motor abundance. Exp Brain Res 163:137–158

    Article  PubMed  Google Scholar 

  41. Zhang W, Scholz JP, Zatsiorsky VM, Latash ML (2008) What do synergies do? Effects of secondary constraints on multi-digit synergies in accurate force-production tasks. J Neurophysiol 99:500–513

    Article  PubMed  Google Scholar 

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Correspondence to Mohsen Shafizadeh.

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Appendix: Details of the UCM method adapted from Krishnan et al (2013)

Appendix: Details of the UCM method adapted from Krishnan et al (2013)

Creating a model for elemental variables

The UCM method that is used in this study has 10 elemental variables and 1 performance variable (mediolateral trajectory of swing foot). A geometric model was created to relate elemental variables to the performance variable. This comprised a five-segment and 10 DoFs model (Fig. 1). The segments were stance leg, pelvis, swing-leg thigh and swing-leg shank with lengths L1, L2, L3, and L4 respectively. For the paretic swing leg in gait cycle, 5 DoFs are in the frontal plane, Ɵ1 = stance-leg shanknon-paretic, Ɵ2 = stance-leg thighnon-paretic, Ɵ3 = pelvis, Ɵ4 swing-leg thighparetic and Ɵ5 = swing-leg shankparetic, and 4 DoFs are out of the frontal plane; α1 = stance-leg shanknon-paretic, α2 = stance-leg thighnon-paretic, α3 = pelvis, α4 = swing-leg thighparetic and s5 = swing-leg shankparetic. The same model is used when for the non-paretic swing leg.

Segmental configuration

An ankle joint trajectory (AJT) and functions of ten DoFs (\(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }\)) from the segmental model was created by a custom-written code in Matlab 2018a (Mathworks, Natick, MA) according to the UCM procedure in Krishnan et al. (2013). Prior to UCM analysis, all segmental configuration and ankle joint trajectory (AJT) data were normalised for swing phases (0–100%):

$${\text{AJT}} = - L_{1} \cos \alpha_{1} \sin \theta_{1} - L_{2} \cos \alpha_{2} \sin \theta_{2} + L_{3} \cos \alpha_{3} \cos \theta_{3} + L_{4} \cos \alpha_{4} \sin \theta_{4} + L_{5} \cos \alpha_{5} \sin \theta_{5}$$
$$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta } = \left[ {\theta_{1} \theta_{2} \theta_{3} \theta_{4} \theta_{5} \alpha_{1} \alpha_{2} \alpha_{3} \alpha_{4} \alpha_{5} } \right].$$
(1)

Calculation of V UCM and V ORT

As geometric models describing the position of an end effector are generally non-linear, uncontrolled manifolds are often curved. Therefore, the first step in calculating \(V_{\text{UCM}}\) and \(V_{\text{ORT}}\) is to perform a linearization around a reference configuration. A linear approximation of the model can be obtained by calculating the Jacobian matrix with respect to a reference configuration—the mean segment configuration across trials (Scholz and Schoner 1999). Deviations of segment vectors, for a particular trial, from the mean segment configuration can then be projected onto the null space (\(\varepsilon\)) of this Jacobian matrix:

$$J = \frac{\partial D}{\partial \varTheta } = \left[ \begin{aligned} - & L_{1} \cos \alpha_{1} \cos \theta_{1} , - L_{2} \cos \alpha_{2} \cos \theta_{2} , - L_{3} \cos \alpha_{3} \sin \theta_{3} ,L_{4} \cos \alpha_{4} \cos \theta_{4} ,L_{5} \cos \alpha_{5} \cos \theta_{5} ,\; \\ L_{1} \sin \alpha_{1} \sin \theta_{1} ,L_{2} \sin \alpha_{2} \sin \theta_{2} , - L_{3} \sin \alpha_{3} \cos \theta_{3} , - L_{4} \sin \alpha_{4} \sin \theta_{4} - L_{5} \sin \alpha_{5} \sin \theta_{5} \\ \end{aligned} \right]$$
$$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }_{\parallel } = \mathop \sum \limits_{i = 1}^{n - d} \left( {\varepsilon_{i} \cdot \left( {\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta } - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }^{0} } \right)} \right)\varepsilon_{i} s,$$
(2)

where \(n\) is the number of degrees of freedom of the model, \(d\) is the number of dimensions of the performance variable, \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }\) is a vector of segment angles for a particular trial and \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }^{0}\) is a vector of segment angles in the reference configuration. For this work, n = 10 and d = 1. A component of the deviation of \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }\) from \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }^{0}\) that is perpendicular to the UCM can also be calculated as:

$$\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }_{ \bot } = \left( {\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta } - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }^{0} } \right) - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\varTheta }_{\parallel } .$$
(3)

The variability per degree of freedom parallel (\(V_{\text{UCM}}\)) and perpendicular (\(V_{\text{ORT}}\)) to the UCM was then calculated using:

$$V_{\text{UCM}} = \left( {\frac{{\sum \varTheta_{\parallel }^{2} }}{{\left( {n - d} \right)N}}} \right),$$
(4)

and

$$V_{\text{ORT}} = \left( {\frac{{\sum \varTheta_{ \bot }^{2} }}{dN}} \right),$$
(5)

where N is the number of stride. VUCM does not affect the variance in the mediolateral foot trajectory (Good variability), whereas VORT causes the variance in the foot trajectory (Bad variability).

Motor synergy index

If VUCM > VORT, then lower limb synergy stabilise the foot trajectory in a gait cycle. A synergy index was calculated as:

$$\Delta V = \frac{{V_{\text{UCM}} - V_{\text{ORT}} }}{{V_{\text{ToT}} }},$$
(6)

where

$$V_{\text{ToT}} = \left( {\frac{1}{n}} \right)\left( {dV_{\text{ORT}} + \left( {n - d} \right)V_{\text{UCM}} } \right),$$
(7)

n = number of DoFs; d = number of performance variable.

The more positive ΔV, the stronger the motor synergy among segments. If VORT = 0, then ΔV = 10/9 and all variance lie within the UCM. If all variance lies in the orthogonal sub-space (VUCM = 0), ΔV = − 10. For consistency among studies, ΔV was transformed to Fisher’s z-transformation index:

$$\Delta V_{Z} = \frac{1}{2}\log \left[ {\frac{10 + \Delta V}{{\frac{10}{9} - \Delta V}}} \right].$$
(8)

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Shafizadeh, M., Wheat, J., Kelley, J. et al. Stroke survivors exhibit stronger lower extremity synergies in more challenging walking conditions. Exp Brain Res 237, 1919–1930 (2019). https://doi.org/10.1007/s00221-019-05560-9

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Keywords

  • Gait
  • Stroke
  • Uncontrolled manifold
  • Synergies