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Portable Wearable and Wireless Systems for Gait and Reflex Response Quantification

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

Abstract

With the advent of wireless technology and inertial measurement units, the prevalence of wireless accelerometers is addressed for quantification of gait, reflex response, and reflex latency. Over the course of four generations of research, development, testing, and evaluation the ability to quantify patellar tendon reflex response and latency has been achieved in an accurate, reliable, and reproducible manner. As a transitional phase to the research, development, testing, and evaluation cycle an artificial reflex device was also applied. The central themes to the wireless quantified reflex device are tandem operated wireless accelerometer nodes that are effectively wearable for deriving response and latency and a potential energy impact pendulum for evoking the patellar tendon reflex. The successful application of these wireless accelerometers that are wearable has been further extended toward the quantification of hemiplegic gait, and real-time modification of hemiplegic gait through the quantified feedback of Virtual Proprioception. Other developments regarding the use of wireless accelerometers that are wearable are further addressed.

Keywords

Wireless accelerometer Patellar tendon reflex Reflex response Reflex latency Wireless quantified reflex device Impact pendulum 

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    LeMoyne RC (2010) Wireless quantified reflex device. Ph.D. Dissertation UCLAGoogle Scholar
  5. 5.
    LeMoyne R, Jafari R, Jea D (2005) Fully quantified evaluation of myotatic stretch reflex. In: 35th Society for Neuroscience Annual MeetingGoogle Scholar
  6. 6.
    LeMoyne R, Dabiri F, Jafari R (2008) Quantified deep tendon reflex device, second generation. J Mech Med Biol 8(01):75–85CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    LeMoyne R, Coroian C, Mastroianni T (2009) Evaluation of a wireless three dimensional MEMS accelerometer reflex quantification device using an artificial reflex system. In: ICME International Conference on IEEE, Complex Medical Engineering (CME), pp 1–5Google Scholar
  10. 10.
    LeMoyne R, Coroian C, Mastroianni T (2009) Wireless accelerometer reflex quantification system characterizing response and latency. In: 31st Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 5283–5286Google Scholar
  11. 11.
    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
  12. 12.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2009) Wireless accelerometer assessment of gait for quantified disparity of hemiparetic locomotion. J Mech Med Biol 9(03):329–343CrossRefGoogle Scholar
  13. 13.
    LeMoyne R, Coroian C, Mastroianni T (2009) Wireless accelerometer system for quantifying gait. In: ICME International Conference on IEEE, Complex Medical Engineering (CME), pp 1–4Google Scholar
  14. 14.
    LeMoyne R, Mastroianni T, Grundfest W (2013) Wireless accelerometer system for quantifying disparity of hemiplegic gait using the frequency domain. J Mech Med Biol 13(03):1350035CrossRefGoogle Scholar
  15. 15.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Virtual proprioception. J Mech Med Biol 8(03):317–338CrossRefGoogle Scholar
  16. 16.
    LeMoyne R, Coroian C, Mastroianni T, Wu W, Grundfest W, Kaiser W (2008) Virtual proprioception with real-time step detection and processing. In: 30th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4238–4241Google Scholar
  17. 17.
    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
  18. 18.
    LeMoyne R, Mastroianni T (2017) Wearable and wireless gait analysis platforms: smartphones and portable media devices. Wireless MEMS Networks and Applications, 129–152Google Scholar
  19. 19.
    Yeoh WS, Pek I, Yong YH, Chen X, Waluyo AB (2008) Ambulatory monitoring of human posture and walking speed using wearable accelerometer sensors. In: 30th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 5184–5187Google Scholar
  20. 20.
    Hsu CC, Chen JH (2011) A novel sensor-assisted RFID-based indoor tracking system for the elderly living alone. Sensors 11(11):10094–10113CrossRefGoogle Scholar
  21. 21.
    Watanabe T, Saito H, Koike E, Nitta K (2011) A preliminary test of measurement of joint angles and stride length with wireless inertial sensors for wearable gait evaluation system. Comput Intell Neurosci 1(2011):6Google Scholar
  22. 22.
    Bugané F, Benedetti MG, Casadio G, Attala S, Biagi F, Manca M, Leardini A (2012) Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: validation on normal subjects by standard gait analysis. Comput Methods Programs Biomed 108(1):129–137CrossRefGoogle Scholar
  23. 23.
    Lai DT, Charry E, Begg R, Palaniswami MA (2008) prototype wireless inertial-sensing device for measuring toe clearance. In: 30th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4899–4902Google Scholar
  24. 24.
    Guo Y, Wu D, Liu G, Zhao G, Huang B, Wang L (2012) A low-cost body inertial-sensing network for practical gait discrimination of hemiplegia patients. Telemedicine e-Health 18(10):748–754CrossRefGoogle Scholar
  25. 25.
    Mizuike C, Ohgi S, Morita S (2009) Analysis of stroke patient walking dynamics using a tri-axial accelerometer. Gait Posture 30(1):60–64CrossRefGoogle Scholar
  26. 26.
    Prajapati SK, Gage WH, Brooks D, Black SE, McIlroy WE (2011) A novel approach to ambulatory monitoring: investigation into the quantity and control of everyday walking in patients with subacute stroke. Neurorehabilitation Neural Repair 25(1):6–14CrossRefGoogle 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

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