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A Novel Approach to Quantify Motion Impairment

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Human-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 145))

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Abstract

The accurate assessment of upper limb motion impairment induced by stroke—which represents one of the primary causes of disability world-wide—is the first step to successfully monitor and guide patients’ recovery. As of today, the majority of the procedures relies on clinical scales, which are mostly based on ordinal scaling, operator-dependent, and subject to floor and ceiling effects. In this work, we intend to overcome these limitations by proposing a novel approach to analytically evaluate the level of pathological movement coupling, based on the quantification of movement complexity. To this goal, we consider the variations of functional Principal Components applied to the reconstruction of joint angle trajectories of the upper limb during daily living task execution, and compared these variations between two conditions, i.e. the affected and non-affected arm. A Dissimilarity Index, which codifies the severity of the upper limb motor impairment with respect to the movement complexity of the non-affected arm, is then proposed. This methodology was validated as a proof of concept upon a set of four chronic stroke subjects with mild to moderate arm and hand impairments. As a first step, we evaluated whether the derived outcomes differentiate between the two conditions upon the whole data-set. Secondly, we exploited this concept to discern between different subjects and impairment levels. Results show that: (i) differences in terms of movement variability between the affected and non-affected upper limb are detectable and (ii) different impairment profiles can be characterized for single subjects using the proposed approach.

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References

  1. Abdel-Malek, K., Yang, J., Brand, R., Tanbour, E.: Towards understanding the workspace of human limbs. Ergonomics 47(13), 1386–1405 (2004)

    Article  Google Scholar 

  2. Alt Murphy, M., Häger, C.K.: Kinematic analysis of the upper extremity after Stroke-how far have we reached and what have we grasped? Phys. Ther. Rev. 20(3), 137–155 (2015)

    Article  Google Scholar 

  3. Averta, G., Angelini, F., Bonilla, M., Bianchi, M., Bicchi, A.: Incrementality and hierarchies in the enrollment of multiple synergies for grasp planning. IEEE Robot. Autom. Lett. 3(3), 2686–2693 (2018)

    Article  Google Scholar 

  4. Averta, G., Barontini, F., Catrambone, V., Haddadin, S., Handjaras, G., Held, J.P., Hu, T., Jakubowitz, E., Kanzler, C.M., Kühn, J., et al.: U-limb: a multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions. GigaScience 10(6), giab043 (2021)

    Google Scholar 

  5. Averta, G., Della Santina, C., Battaglia, E., Felici, F., Bianchi, M., Bicchi, A.: Unvealing the principal modes of human upper limb movements through functional analysis. Front. Robot. AI 4, 37 (2017)

    Article  Google Scholar 

  6. Basteris, A., Contu, S., Plunkett, T.K., Kuah, C.W., Konczak, I.J., Chua, K.S., Masia, L.: Robot-aided bimanual assessment of wrist proprioception in people with acute stroke. In: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 473–478. IEEE (2018)

    Google Scholar 

  7. Camardella, C., Murciego, L.P., Tang, S., Bertolucci, F., Chisari, C., Barsotti, M., Frisoli, A.: Simple tool for functional and physiological stroke patients assessment. In: International Conference on NeuroRehabilitation, pp. 779–782. Springer (2018)

    Google Scholar 

  8. Catrambone, V., Greco, A., Averta, G., Bianchi, M., Valenza, G., Scilingo, E.P.: Predicting object-mediated gestures from brain activity: an EEG study on gender differences. IEEE Trans. Neural Syst. Rehabil. Eng. 27(3), 411–418 (2019). https://doi.org/10.1109/TNSRE.2019.2898469

    Article  Google Scholar 

  9. Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans. Robot. Autom. 5(3), 269–279 (1989)

    Article  Google Scholar 

  10. Della Santina, C., Bianchi, M., Averta, G., Ciotti, S., Arapi, V., Fani, S., Battaglia, E., Catalano, M.G., Santello, M., Bicchi, A.: Postural hand synergies during environmental constraint exploitation. Front. Neurorobot. 11, 41 (2017)

    Article  Google Scholar 

  11. Feigin, V.L., Forouzanfar, M.H., Krishnamurthi, R., Mensah, G.A., Connor, M., Bennett, D.A., Moran, A.E., Sacco, R.L., Anderson, L., Truelsen, T., et al.: Global and regional burden of stroke during 1990–2010: findings from the global burden of disease study 2010. Lancet 383(9913), 245–255 (2014)

    Article  Google Scholar 

  12. Feix, T., Romero, J., Schmiedmayer, H.B., Dollar, A.M., Kragic, D.: The grasp taxonomy of human grasp types. IEEE Trans. Hum.-Mach. Syst. 46(1), 66–77 (2016)

    Article  Google Scholar 

  13. Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand. J. Rehabil. Med. 7(1), 13–31 (1975)

    Google Scholar 

  14. Gladstone, D.J., Danells, C.J., Black, S.E.: The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil. Neural Repair 16(3), 232–240 (2002)

    Article  Google Scholar 

  15. Heidari, O., Roylance, J.O., Perez-Gracia, A., Kendall, E.: Quantification of upper-body synergies: a case comparison for stroke and non-stroke victims. In: ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V05AT07A032–V05AT07A032. American Society of Mechanical Engineers (2016)

    Google Scholar 

  16. Held, J.P., Klaassen, B., Eenhoorn, A., Beijnum, B.J.F.V., Buurke, J.H., Veltink, P.H., Luft, A.R.: Inertial sensor measurements of upper-limb kinematics in stroke patients in clinic and home environment. Front. Bioeng. Biotechnol. 6, 27 (2018)

    Google Scholar 

  17. van Kordelaar, J., van Wegen, E.E., Kwakkel, G.: Unraveling the interaction between pathological upper limb synergies and compensatory trunk movements during reach-to-grasp after stroke: a cross-sectional study. Exp. Brain Res. 221(3), 251–262 (2012)

    Article  Google Scholar 

  18. Kwakkel, G., Lannin, N.A., Borschmann, K., English, C., Ali, M., Churilov, L., Saposnik, G., Winstein, C., Van Wegen, E.E., Wolf, S.L., et al.: Standardized measurement of sensorimotor recovery in stroke trials: consensus-based core recommendations from the stroke recovery and rehabilitation roundtable. Neurorehabil. Neural Repair 31(9), 784–792 (2017)

    Article  Google Scholar 

  19. Lambercy, O., Fluet, M.C., Lamers, I., Kerkhofs, L., Feys, P., Gassert, R.: Assessment of upper limb motor function in patients with multiple sclerosis using the virtual peg insertion test: a pilot study. In: 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–6. IEEE (2013)

    Google Scholar 

  20. Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet 377(9778), 1693–1702 (2011)

    Article  Google Scholar 

  21. Latash, M.L.: Synergy. Oxford University Press (2008). https://doi.org/10.1093/acprof:oso/9780195333169.001.0001

  22. Lenarcic, J., Umek, A.: Simple model of human arm reachable workspace. IEEE Trans. Syst. Man Cybern. 24(8), 1239–1246 (1994)

    Article  Google Scholar 

  23. Lorussi, F., Carbonaro, N., De Rossi, D., Paradiso, R., Veltink, P., Tognetti, A.: Wearable textile platform for assessing stroke patient treatment in daily life conditions. Front. Bioeng. Biotechnol. 4, 28 (2016)

    Article  Google Scholar 

  24. Osu, R., Ota, K., Fujiwara, T., Otaka, Y., Kawato, M., Liu, M.: Quantifying the quality of hand movement in stroke patients through three-dimensional curvature. J. Neuroeng. Rehabil. 8(1), 62 (2011)

    Article  Google Scholar 

  25. Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASME Trans. Mechatron. 12(4), 408 (2007)

    Article  Google Scholar 

  26. Raghavan, P.: Upper limb motor impairment after stroke. Phys. Med. Rehabil. Clin. 26(4), 599–610 (2015)

    Article  Google Scholar 

  27. Ramsay, J.O.: Functional data analysis. Wiley Online Library (2006)

    Google Scholar 

  28. Reisman, D.S., Scholz, J.P.: Aspects of joint coordination are preserved during pointing in persons with post-stroke hemiparesis. Brain 126(11), 2510–2527 (2003)

    Article  Google Scholar 

  29. Santello, M.: Synergistic control of hand muscles through common neural input. In: The Human Hand as an Inspiration for Robot Hand Development, pp. 23–48. Springer (2014)

    Google Scholar 

  30. Santello, M., Lang, C.E.: Are movement disorders and sensorimotor injuries pathologic synergies? when normal multi-joint movement synergies become pathologic. Front. Hum. Neurosci. 8, 1050 (2015)

    Article  Google Scholar 

  31. Santisteban, L., Térémetz, M., Bleton, J.P., Baron, J.C., Maier, M.A., Lindberg, P.G.: Upper limb outcome measures used in stroke rehabilitation studies: a systematic literature review. PLoS One 11(5), e0154792 (2016)

    Google Scholar 

  32. Schwarz, A., Averta, G., Veerbeek, J.M., Luft, A., Held, J.P., Valenza, G., Bicchi, A., Bianchi, M.: A functional analysis-based approach to quantify upper limb impairment level in chronic stroke patients: a pilot study. In: Engineering in Medicine and Biology Society, 2019. EMBC’19. 41th Annual International Conference of the IEEE. IEEE (2019)

    Google Scholar 

  33. See, J., Dodakian, L., Chou, C., Chan, V., McKenzie, A., Reinkensmeyer, D.J., Cramer, S.C.: A standardized approach to the Fugl-Meyer assessment and its implications for clinical trials. Neurorehabil. Neural Repair 27(8), 732–741 (2013)

    Article  Google Scholar 

  34. Suresh, N.L., Zhou, P., Rymer, W.Z.: Abnormal emg-force slope estimates in the first dorsal interosseous of hemiparetic stroke survivors. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 3562–3565. IEEE (2008)

    Google Scholar 

  35. Thrane, G., Sunnerhagen, K.S., Persson, H.C., Opheim, A., Alt Murphy, M.: Kinematic upper extremity performance in people with near or fully recovered sensorimotor function after stroke. Physiother. Theory Pract. 1–11 (2018)

    Google Scholar 

  36. Tresch, M.C., Jarc, A.: The case for and against muscle synergies. Curr. Opin. Neurobiol. 19(6), 601–607 (2009)

    Article  Google Scholar 

  37. Xsens Technologies, B.V.: MVN User Manual. MVN Awinda. Rev. March, User Guide MVN, MVN BIOMECH MVN Link (2017)

    Google Scholar 

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Authors and Affiliations

  1. Research Center E. Piaggio, University of Pisa, Pisa, Italy

    Giuseppe Averta

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  1. Giuseppe Averta
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Averta, G. (2022). A Novel Approach to Quantify Motion Impairment. In: Human-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices. Springer Tracts in Advanced Robotics, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-030-92521-5_6

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