Advertisement

Bluetooth Inertial Sensors for Gait and Reflex Response Quantification with Perspectives Regarding Cloud Computing and the Internet of Things

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

Abstract

Bluetooth wireless enables localized connectivity to a smartphone, portable media device, and tablet. Rather than using these devices as wearable and wireless systems alone, the nature of Bluetooth wireless enables locally situated inertial sensors to be mounted to a subject for quantified evaluation of gait. The smartphone, portable media device, and tablet can then wirelessly transmit the data to a Cloud Computing resource for post-processing. Preliminary demonstration is presented regarding the machine learning classification of gait for Friedreich’s ataxia. A perspective of the application of Bluetooth wireless for reflex quantification is presented. Themes, such as sensor fusion and the Internet of Things, are further discussed. The prevalence of Bluetooth wireless further establishes the realization of Network Centric Therapy.

Keywords

Bluetooth wireless Smartphone Portable media device Table Inertial sensor node Machine learning Cloud Computing Gait Friedreich’s ataxia Reflex quantification Sensor fusion Internet of Things 

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, 165–198 Google 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. PhD 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 Bio 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 Bio 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 Bio 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 Bio 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 Bio 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 Bio 13(03):1350035CrossRefGoogle Scholar
  15. 15.
    LeMoyne R, Coroian C, Mastroianni T, Grundfest W (2008) Virtual proprioception. J Mech Med Bio 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.
    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
  20. 20.
    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
  21. 21.
    LeMoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W (2010) Implementation of an iPhone as a wireless accelerometer for quantifying gait characteristics. In: 32nd Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3847–3851Google Scholar
  22. 22.
    LeMoyne R, Mastroianni T, Cozza M, Coroian C (2010) iPhone wireless accelerometer application for acquiring quantified gait attributes. In: ASME 2010 5th Frontiers in Biomedical Devices Conference, American Society of Mechanical Engineers, pp 19–20Google Scholar
  23. 23.
    LeMoyne R, Mastroianni T, Cozza M, Coroian C (2010) Quantification of gait characteristics through a functional iPhone wireless accelerometer application mounted to the spine. In: ASME 2010 5th Frontiers in Biomedical Devices Conference, American Society of Mechanical Engineers, pp 87–88Google Scholar
  24. 24.
    LeMoyne R, Mastroianni T (2014) Quantification of patellar tendon reflex response using an iPod wireless gyroscope application with experimentation conducted in Lhasa, Tibet and post-processing conducted in Flagstaff, Arizona through wireless Internet connectivity. In: 44th Society for Neuroscience Annual MeetingGoogle Scholar
  25. 25.
    LeMoyne R, Mastroianni T, Grundfest W (2011) Wireless accelerometer iPod application for quantifying gait characteristics. In: 33rd Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 7904–7907Google Scholar
  26. 26.
    LeMoyne R, Mastroianni T (2014) Implementation of an iPod application as a wearable and wireless accelerometer system for identifying quantified disparity of hemiplegic gait. J Med Imag Health Inform 4(4):634–641CrossRefGoogle Scholar
  27. 27.
    LeMoyne R, Mastroianni T, Montoya K (2014) Implementation of a smartphone for evaluating gait characteristics of a trans-tibial prosthesis. In: 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3674–3677Google Scholar
  28. 28.
    LeMoyne R, Mastroianni T (2016) Implementation of a smartphone as a wireless gyroscope platform for quantifying reduced arm swing in hemiplegic gait with machine learning classification by multilayer perceptron neural network. In: 38th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 2626–2630Google Scholar
  29. 29.
    Mastroianni T, LeMoyne R (2016) Application of a multilayer perceptron neural network with an iPod as a wireless gyroscope platform to classify reduced arm swing gait for people with Erb’s palsy. In: 46th Society for Neuroscience Annual MeetingGoogle Scholar
  30. 30.
    LeMoyne R, Mastroianni T, Grundfest W (2012) Quantified reflex strategy using an iPod as a wireless accelerometer application. In: 34th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 2476–2479Google Scholar
  31. 31.
    LeMoyne R, Mastroianni T (2011) Reflex response quantification using an iPod wireless accelerometer application. In: 41st Society for Neuroscience Annual MeetingGoogle Scholar
  32. 32.
    LeMoyne R, Kerr WT, Zanjani K, Mastroianni T (2014) Implementation of an iPod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar tendon reflex pair. J Med Imag Health Inform 4(1):21–28CrossRefGoogle Scholar
  33. 33.
    LeMoyne R, Mastroianni T, Grundfest W, Nishikawa K (2013) Implementation of an iPhone wireless accelerometer application for the quantification of reflex response. In: 35th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4658–4661Google Scholar
  34. 34.
    LeMoyne R, Mastroianni T (2014) Implementation of a smartphone as a wireless gyroscope application for the quantification of reflex response. In: 36th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 3654–3657Google Scholar
  35. 35.
    LeMoyne R, Mastroianni T (2015) Machine learning classification of a hemiplegic and healthy patellar tendon reflex pair through an iPod wireless gyroscope platform. In: 45th Society for Neuroscience Annual MeetingGoogle Scholar
  36. 36.
    LeMoyne R, Mastroianni T (2016) Implementation of a multilayer perceptron neural network for classifying a hemiplegic and healthy reflex pair using an iPod wireless gyroscope platform. In: 46th Society for Neuroscience Annual MeetingGoogle Scholar
  37. 37.
    LeMoyne R, Mastroianni T (2016) Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar tendon reflex pair disparity through a multilayer perceptron neural network. In: Wireless Health (WH) of IEEE, pp 1–6Google Scholar
  38. 38.
    LeMoyne R, Mastroianni T (2017) Implementation of a smartphone wireless gyroscope platform with machine learning for classifying disparity of a hemiplegic patellar tendon reflex pair. J Mech Med Bio (Online Ready):1750083Google Scholar
  39. 39.
  40. 40.
    LeMoyne R, Heerinckx F, Aranca T, De Jager R, Zesiewicz T, Saal HJ (2016) Wearable body and wireless inertial sensors for machine learning classification of gait for people with Friedreich’s ataxia. In: IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp 147–151Google Scholar
  41. 41.
    Texas Instruments [Sensor Tag www.ti.com/sensortag]
  42. 42.
    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
  43. 43.
    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. Telemed e-Health 18(10):748–754CrossRefGoogle Scholar
  44. 44.
    González I, Fontecha J, Hervás R, Bravo J (2015) An ambulatory system for gait monitoring based on wireless sensorized insoles. Sensors 15(7):16589–16613CrossRefGoogle Scholar
  45. 45.
    Wagner R, Ganz A (2012) PAGAS: portable and accurate gait analysis system. In:34th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 280–283Google Scholar
  46. 46.
    Pu F, Fan X, Yang Y, Chen W, Li S, Li D, Fan Y (2014) Feedback system based on plantar pressure for monitoring Toe-walking strides in children with Cerebral Palsy. Am J Phys Med Rehabil 93(2):122–129CrossRefGoogle Scholar
  47. 47.
    Casamassima F, Ferrari A, Milosevic B, Ginis P, Farella E, Rocchi L (2014) A wearable system for gait training in subjects with Parkinson’s disease. Sensors 14(4):6229–6246CrossRefGoogle Scholar
  48. 48.
    Rosevall J, Rusu C, Talavera G, Carrabina J, Garcia J, Carenas C, Breuil F, Reixach E, Torrent M, Burkard S (2014) A wireless sensor insole for collecting gait data. Stud Heal Technol Inf 30(200):176–178Google Scholar
  49. 49.
  50. 50.
    Rebula JR, Ojeda LV, Adamczyk PG, Kuo AD (2013) Measurement of foot placement and its variability with inertial sensors. Gait Posture 38(4):974–980CrossRefGoogle Scholar
  51. 51.
    Swan M (2012) Sensor mania! the Internet of Things, wearable computing, objective metrics, and the quantified self 2.0. J Sens Actuator Netw 1(3):217–253Google Scholar
  52. 52.
    LeMoyne R (2016) Future and advanced concepts for the powered prosthesis. Advances for Prosthetic Technology: From Historical Perspective to Current Status to Future Application, 127–130Google 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