Sensor Abstracted Extremity Representation for Automatic Fugl-Meyer Assessment

  • Patrick HeyerEmail author
  • Felipe Orihuela-Espina
  • Luis R. Castrejón
  • Jorge Hernández-Franco
  • Luis Enrique Sucar
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 179)


Given its virtually algorithmic process, the Fugl-Meyer Assessment (FMA) of motor recovery is prone to automatization reducing subjectivity, alleviating therapists’ burden and collaterally reducing costs. Several attempts have been recently reported to achieve such automatization of the FMA. However, a cost-effective solution matching expert criteria is still unfulfilled, perhaps because these attempts are sensor-specific representation of the limb or have thus far rely on a trial and error strategy for building the underpinning computational model. Here, we propose a sensor abstracted representation. In particular, we improve previously reported results in the automatization of FMA by classifying a manifold embedded representation capitalizing on quaternions, and explore a wider range of classifiers. By enhancing the modeling, overall classification accuracy is boosted to 87% (mean: 82% ± 4.53:) well over the maximum reported in literature thus far 51.03% (mean: 48.72 ± std: 2.10). The improved model brings automatic FMA closer to practical usage with implications for rehabilitation programs both in ward and at home.


Automatic motor dexterity assessment Gesture classification Gesture representation Sensor independent representation Automatic Fugl-Meyer 



The leading author has received a scholarship No. 339981 from CONACYT.


  1. 1.
    Adamovich, S.V., Fluet, G.G., Tunik, E., Merians, A.S.: Sensorimotor training in virtual reality: a review. NeuroRehabilitation 25, 29 (2009)Google Scholar
  2. 2.
    Reinkensmeyer, D.J., Pang, C.T., Nessler, J.A., Painter, C.C.: Web-based telerehabilitation for the upper extremity after stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 10, 102–108 (2002)CrossRefGoogle Scholar
  3. 3.
    Krakauer, J.W., Carmichael, S.T., Corbett, D., Wittenberg, G.F.: Getting neurorehabilitation right: what can be learned from animal models? Neurorehabilitation Neural Repair 26, 923–931 (2012)CrossRefGoogle Scholar
  4. 4.
    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, 13–31 (1975)Google Scholar
  5. 5.
    Duncan, P.W., Propst, M., Nelson, S.G.: Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys. Ther. 63, 1606–1610 (1983)Google Scholar
  6. 6.
    Quintana, G.E., et al.: Qualification of arm gestures using hidden markov models. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2008, pp. 1–6. IEEE (2008)Google Scholar
  7. 7.
    Hou, W.-H., Shih, C.-L., Chou, Y.-T., Sheu, C.-F., Lin, J.-H., Wu, H.-C., Hsueh, I.-P., Hsieh, C.-L.: Development of a computerized adaptive testing system of the Fugl-Meyer motor scale in stroke patients. Arch. Phys. Med. Rehabil. 93, 1014–1020 (2012)CrossRefGoogle Scholar
  8. 8.
    Ma, V.Y., Chan, L., Carruthers, K.J.: The incidence, prevalence, costs and impact on disability of common conditions requiring rehabilitation in the US: stroke, spinal cord injury, traumatic brain injury, multiple sclerosis, osteoarthritis, rheumatoid arthritis, limb loss, and back pain. Arch. Phy. Med. Rehabil. 95(5), 986–995.e1 (2014)CrossRefGoogle Scholar
  9. 9.
    Allin, S., Ramanan, D.: Assessment of post-stroke functioning using machine vision. In: MVA2007 IAPR Conference on Machine Vision Applications, 16-18 May, Tokyo, Japan, pp. 8–18 (2007)Google Scholar
  10. 10.
    Virgilio, F.B., Cruz, V.T., Ribeiro, D.D., Cunha, J.P.: Towards a movement quantification system capable of automatic evaluation of upper limb motor function after neurological injury. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 5456–5460. IEEE (2011)Google Scholar
  11. 11.
    Hester, T., Hughes, R., Sherrill, D.M., Knorr, B., Akay, M., Stein, J., Bonato, P.: Using wearable sensors to measure motor abilities following stroke. In: International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2006, p. 4. IEEE (2006)Google Scholar
  12. 12.
    Balasubramanian, S., Wei, R., Perez, M., Shepard, B., Koeneman, J., Koeneman, E., He, J.: RUPERT: an exoskeleton robot for assisting rehabilitation of arm functions. In: Virtual Rehabilitation, 163–167. IEEE (2008)Google Scholar
  13. 13.
    Sucar, L.E., Orihuela-Espina, F., Velazquez, R.L., Reinkensmeyer, D.J., Leder, R., Hernández Franco, J.: Gesture therapy: an upper limb virtual reality-based motor rehabilitation platform. IEEE Trans. Neural Syst. Rehabil. Eng. 22(3), 634–643 (2014)CrossRefGoogle Scholar
  14. 14.
    der Maaten, V.L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  15. 15.
    Murphy, K.P.: Naive Bayes classifiers. University of British Columbia (2006)Google Scholar
  16. 16.
    Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43, 1947–1958 (2003)CrossRefGoogle Scholar
  17. 17.
    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13, 18–28 (1998)CrossRefGoogle Scholar
  18. 18.
    Olesh, E.V., Yakovenko, S., Gritsenko, V.: Automated assessment of upper extremity movement impairment due to stroke. PLoS ONE 9(8), e104487 (2014)CrossRefGoogle Scholar
  19. 19.
    Wade, E., Parnandi, A.R., Matarić, M.J.: Automated administration of the Wolf Motor Function test for post-stroke assessment. In: 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Munich, Germany, pp. 1–7 (2010)Google Scholar
  20. 20.
    Hondori, H.M., Ling, S.-F.: A method for measuring human arm’s mechanical impedance for assessment of motor rehabilitation. In: 3rd International Convention on Rehabilitation Engineering & Assistive Technology (i-CREATe 2009), Singapore, p. 4 (2009)Google Scholar
  21. 21.
    Carreira-Perpiñán, M.A.: A review of dimension reduction techniques University of Sheffield, University of Sheffield, Technical report, CS-96-09 (1997)Google Scholar
  22. 22.
    Heyer, P., Felipe, O.-E., Castrejón, L.R., Hernández-Franco, J., Sucar, L.E.: Sensor adequacy and arm movement encoding for automatic assessment of motor dexterity for virtual rehabilitation. Accepted at 9th World Congress for NeuroRehabilitationGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Patrick Heyer
    • 1
    Email author
  • Felipe Orihuela-Espina
    • 1
  • Luis R. Castrejón
    • 2
  • Jorge Hernández-Franco
    • 3
  • Luis Enrique Sucar
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico
  2. 2.Hospital Universitario de la Benemérita Universidad Autónoma de Puebla (HU-BUAP)PueblaMexico
  3. 3.Instituto Nacional de Neurología y Neurocirugía (INNN)Mexico CityMexico

Personalised recommendations