Advertisement

Quantification Systems Appropriate for a Clinical Setting

  • Robert LeMoyneEmail author
  • Timothy Mastroianni
Chapter
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 27)

Abstract

Conventional gait quantification is provided in a highly structured clinical setting. These devices represent a metaphorical second wave encompassing clinically standard quantification techniques. Traditional gait quantification systems, such as force plates, EMG, foot-switches, and motion capture systems are described in the chapter for gait analysis. Their relevance for objectively quantifying the status of a patient’s rehabilitation progress is advocated. Regarding reflex quantification the application of motion capture systems, EMG, and strain/force sensors are covered in the chapter. There are drawbacks of these devices, such as expense, complexity, and limitations to a clinical setting. By contrast, wearable and wireless systems are projected to transcend the capabilities of these traditional quantification systems with expanded autonomy for subject evaluation in the context of Network Centric Therapy.

Keywords

Quantification Gait analysis Reflex response Tendon reflex Foot switches Electrogoniometers Electromyogram (EMG) Metabolic analysis Optical motion cameras Force plates Machine learning 

References

  1. 1.
    Dobkin BH (2003) The clinical science of neurologic rehabilitation. Oxford University PressGoogle Scholar
  2. 2.
    Perry J (1992) Gait analysis: normal and pathological function. SlackGoogle Scholar
  3. 3.
    LeMoyne R, Mastroianni T (2015) Use of smartphones and portable media devices for quantifying human movement characteristics of gait, tendon reflex response, and Parkinson’s disease hand tremor. Methods and Protocols, Mobile Health Technologies, 335–358Google Scholar
  4. 4.
    LeMoyne R, Mastroianni T (2017) Wearable and wireless gait analysis platforms: smartphones and portable media devices. Wireless MEMS Networks and Applications, 129–152Google Scholar
  5. 5.
    LeMoyne R, Mastroianni T (2016) Telemedicine perspectives for wearable and wireless applications serving the domain of neurorehabilitation and movement disorder treatment. Telemedicine, 1–10Google Scholar
  6. 6.
    LeMoyne R, Coroian C, Cozza M, Opalinski P, Mastroianni T, Grundfest W (2009) The merits of artificial proprioception, with applications in biofeedback gait rehabilitation concepts and movement disorder characterization. Biomedical Engineering, 165–198Google Scholar
  7. 7.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Virtual proprioception. J Mech Med Biol 8(03):317–338CrossRefGoogle Scholar
  8. 8.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Accelerometers for quantification of gait and movement disorders: a perspective review. J Mech Med Biol 8(02):137–152CrossRefGoogle Scholar
  9. 9.
    LeMoyne R (2015) Advances regarding powered prosthesis for transtibial amputation. J Mech Med Biol 15(01):1530001CrossRefGoogle Scholar
  10. 10.
    LeMoyne R (2016) Advances for prosthetic technology: from historical perspective to current status to future application. SpringerGoogle Scholar
  11. 11.
    LeMoyne R (2016) Testing and evaluation strategies for the powered prosthesis, a global perspective. Advances for Prosthetic Technology: From Historical Perspective to Current Status to Future Application, 37–58Google Scholar
  12. 12.
    Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38(3):401–408CrossRefGoogle Scholar
  13. 13.
    Begg RK, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 52(5):828–838CrossRefGoogle Scholar
  14. 14.
    LeMoyne R, Kerr W, Mastroianni T, Hessel A (2014) Implementation of machine learning for classifying hemiplegic gait disparity through use of a force plate. In: 13th International Conference on Machine Learning and Applications (ICMLA), IEEE, pp 379–382Google Scholar
  15. 15.
    LeMoyne R, Mastroianni T, Hessel A, Nishikawa K (2015) Implementation of machine learning for classifying prosthesis type through conventional gait analysis. In: 37th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 202–205Google Scholar
  16. 16.
    LeMoyne R, Mastroianni T, Hessel A, Nishikawa K (2015) Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. In: 14th International Conference on Machine Learning and Applications (ICMLA), IEEE, pp 402–405Google Scholar
  17. 17.
    Winter DA (1990) Biomechanics and motor control of human movement. Wiley-InterscienceGoogle Scholar
  18. 18.
    Lee JA, Cho SH, Lee JW, Lee KH, Yang HK. Wearable accelerometer system for measuring the temporal parameters of gait. In: 29th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), 483–486Google Scholar
  19. 19.
    Lee JA, Cho SH, Lee YJ, Yang HK, Lee JW (2010) Portable activity monitoring system for temporal parameters of gait cycles. J Med Syst 34(5):959–966CrossRefGoogle Scholar
  20. 20.
    Saremi K, Marehbian J, Yan X, Regnaux JP, Elashoff R, Bussel B, Dobkin BH (2006) Reliability and validity of bilateral thigh and foot accelerometry measures of walking in healthy and hemiparetic subjects. Neurorehabil Neural Repair 20(2):297–305CrossRefGoogle Scholar
  21. 21.
  22. 22.
  23. 23.
  24. 24.
    LeMoyne R (2016) Ankle-foot complex and the fundamental aspects of gait. Advances for Prosthetic Technology: From Historical Perspective to Current Status to Future Application, 15–27Google Scholar
  25. 25.
    Winter DA (1983) Energy generation and absorption at the ankle and knee during fast, natural, and slow cadences. Clin Orthop Relat Res 1(175):147–154Google Scholar
  26. 26.
    Winter DA, Sienko SE (1988) Biomechanics of below-knee amputee gait. J Biomech 21(5):361–367CrossRefGoogle Scholar
  27. 27.
    Sanderson DJ, Martin PE (1997) Lower extremity kinematic and kinetic adaptations in unilateral below-knee amputees during walking. Gait & Posture 6(2):126–136CrossRefGoogle Scholar
  28. 28.
    LeMoyne RC (2010) Wireless quantified reflex device. Ph.D. Dissertation UCLAGoogle Scholar
  29. 29.
    LeMoyne R, Mastroianni T, Coroian C, Grundfest W (2011) Tendon reflex and strategies for quantification, with novel methods incorporating wireless accelerometer reflex quantification devices, a perspective review. J Mech Med Biol 11(03):471–513CrossRefGoogle Scholar
  30. 30.
    Ishikawa M, Komi PV, Grey MJ, Lepola V, Bruggemann GP (2005) Muscle-tendon interaction and elastic energy usage in human walking. J Appl Physiol 99(2):603–608CrossRefGoogle Scholar
  31. 31.
    Fey NP, Klute GK, Neptune RR (2011) The influence of energy storage and return foot stiffness on walking mechanics and muscle activity in below-knee amputees. Clin Biomech 26(10):1025–1032CrossRefGoogle Scholar
  32. 32.
    Van de Crommert HW, Faist M, Berger W, Duysens J (1996) Biceps femoris tendon jerk reflexes are enhanced at the end of the swing phase in humans. Brain Res 734(1):341–344CrossRefGoogle Scholar
  33. 33.
    Faist M, Ertel M, Berger W, Dietz V (1999) Impaired modulation of quadriceps tendon jerk reflex during spastic gait: differences between spinal and cerebral lesions. Brain 122(3):567–579CrossRefGoogle Scholar
  34. 34.
    Cozens JA, Miller S, Chambers IR, Mendelow AD (2000) Monitoring of head injury by myotatic reflex evaluation. J Neurol Neurosurg Psychiatry 68(5):581–588CrossRefGoogle Scholar
  35. 35.
    Pagliaro P, Zamparo P (1999) Quantitative evaluation of the stretch reflex before and after hydro kinesy therapy in patients affected by spastic paresis. J Electromyogr Kinesiol 9(2):141–148CrossRefGoogle Scholar
  36. 36.
    Zhang LQ, Wang G, Nishida T, Xu D, Sliwa JA, Rymer WZ (2000) Hyperactive tendon reflexes in spastic multiple sclerosis: measures and mechanisms of action. Arch Phys Med Rehabil 81(7):901–909CrossRefGoogle Scholar
  37. 37.
    Koceja DM, Kamen G (1988) Conditioned patellar tendon reflexes in sprint-and endurance-trained athletes. Med Sci Sports Exerc 20(2):172–177CrossRefGoogle Scholar
  38. 38.
    Kamen G, Koceja DM (1989) Contralateral influences on patellar tendon reflexes in young and old adults. Neurobiol Aging 10(4):311–315CrossRefGoogle Scholar
  39. 39.
    Lebiedowska MK, Fisk JR (2003) Quantitative evaluation of reflex and voluntary activity in children with spasticity. Arch Phys Med Rehabil 84(6):828–837CrossRefGoogle Scholar
  40. 40.
    Mamizuka N, Sakane M, Kaneoka K, Hori N, Ochiai N (2007) Kinematic quantitation of the patellar tendon reflex using a tri-axial accelerometer. J Biomech 40(9):2107–2111CrossRefGoogle Scholar
  41. 41.
    Tham LK, Osman NA, Abas WA, Lim KS (2013) The validity and reliability of motion analysis in patellar tendon reflex assessment. PLoS ONE 8(2):e55702CrossRefGoogle Scholar
  42. 42.
    Tham LK, Osman NA, Lim KS, Pingguan-Murphy B, Abas WW, Zain NM (2011) Investigation to predict patellar tendon reflex using motion analysis technique. Med Eng Phys 33(4):407–410CrossRefGoogle Scholar
  43. 43.
    Chandrasekhar A, Osman NA, Tham LK, Lim KS, Abas WA (2013) Influence of age on patellar tendon reflex response. PLoS ONE 8(11):e80799CrossRefGoogle Scholar
  44. 44.
    LeMoyne R, Dabiri F, Jafari R (2008) Quantified deep tendon reflex device, second generation. J Mech Med Biol 8(01):75–85CrossRefGoogle Scholar
  45. 45.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Quantified deep tendon reflex device for response and latency, third generation. J Mech Med Biol 8(04):491–506CrossRefGoogle Scholar
  46. 46.
    LeMoyne R, Mastroianni T, Kale H, Luna J, Stewart J, Elliot S, Bryan F, Coroian C, Grundfest W (2011) Fourth generation wireless reflex quantification system for acquiring tendon reflex response and latency. J Mech Med Biol 11(01):31–54CrossRefGoogle Scholar
  47. 47.
    LeMoyne R, Mastroianni T, Coroian C, Grundfest W (2010) Wireless three dimensional accelerometer reflex quantification device with artificial reflex system. J Mech Med Biol 10(03):401–415CrossRefGoogle Scholar
  48. 48.
    LeMoyne R, Coroian C, Mastroianni T (2009) Wireless accelerometer reflex quantification system characterizing response and latency. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009. 3 September 2009, IEEE. pp 5283–5286Google Scholar
  49. 49.
    Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 9(1):21CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Biological Sciences, Center for Bioengineering InnovationNorthern Arizona UniversityFlagstaffUSA
  2. 2.IndependentPittsburghUSA

Personalised recommendations