Advertisement

Wearable and Wireless Systems with Internet Connectivity for Quantification of Parkinson’s Disease and Essential Tremor Characteristics

  • Robert LeMoyne
  • Timothy Mastroianni
  • Donald Whiting
  • Nestor Tomycz
Chapter
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 31)

Abstract

Wearable and wireless systems for the objective quantification of neurodegenerative movement disorder status, such as Parkinson’s disease, have been successful achieved through the application of a smartphone. Preliminarily, the smartphone represented a wearable and wireless accelerometer system, which could be readily mounted to the dorsum of the hand through a glove. The initial proof-of-concept demonstration had broad implications. The experimental and post-processing resources were situated on effectively opposite sides of the continental United States of America. Through the smartphone’s wireless connectivity to the Internet, the post-processing resources to reduce the data and the experimentation sited could be located effectively anywhere in the world. Furthermore, the experimental location could be selected based on the patient’s preference. Another exemplary wearable and wireless system is the portable media device. As an extension of this wearable and wireless system capability, the smartphone was successfully applied to ascertain from a quantified perspective the efficacy of deep brain stimulation for Essential tremor. Extrapolations of inertial signal data for a wearable and wireless system, such as a smartphone, advocate the application of machine learning classification to distinguish between deep brain stimulation efficacy regarding “On” and “Off” status. Future evolutions of wearable and wireless systems for the objective quantification of neurodegenerative movement disorder status, such as Parkinson’s disease and Essential tremor, underscore the value of local wireless connectivity from an inertial sensor node to a more powerful wireless system, such as a smartphone or tablet, to achieve Internet connectivity. These trends provide preliminary realization of the opportunities that Network Centric Therapy can enable with inertial sensor signal data stored in a Cloud computing database for post-processing to achieve patient-specific intervention and optimized deep brain stimulation parameter configurations.

Keywords

Wearable and wireless system Smartphone Portable media device Smartwatch Tablet Wireless Internet connectivity Bluetooth wireless Inertial sensor Accelerometer Gyroscope Parkinson’s disease Essential tremor 

References

  1. 1.
    LeMoyne R, Mastroianni T (2018) Wearable and wireless systems for healthcare I: gait and reflex response quantification. Springer, SingaporeCrossRefGoogle Scholar
  2. 2.
    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. In: Biomedical engineering. InTech, Vienna, pp 165–198Google Scholar
  3. 3.
    LeMoyne R, Mastroianni T (2017) Smartphone and portable media device: a novel pathway toward the diagnostic characterization of human movement. In: Smartphones from an applied research perspective. InTech, Rijeka, Croatia, pp 1–24Google Scholar
  4. 4.
    LeMoyne R, Mastroianni T (2017) Wearable and wireless gait analysis platforms: smartphones and portable media devices. In: Wireless MEMS networks and applications. Elsevier, New York, pp 129–152CrossRefGoogle Scholar
  5. 5.
    LeMoyne R, Mastroianni T (2016) Telemedicine perspectives for wearable and wireless applications serving the domain of neurorehabilitation and movement disorder treatment. In: Telemedicine, SMGroup, Dover, Delaware, pp 1–10Google Scholar
  6. 6.
    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. In: Mobile health technologies, methods and protocols. Springer, New York, pp 335–358Google Scholar
  7. 7.
    LeMoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W (2010) Implementation of an iPhone for characterizing Parkinson’s disease tremor through a wireless accelerometer application. In: 32nd annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 4954–4958Google Scholar
  8. 8.
    Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C, Baloyiannis S (2011) Towards remote evaluation of movement disorders via smartphones. In: 33rd annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 5240–5243Google Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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 Biol 17(6):1750083CrossRefGoogle Scholar
  12. 12.
    LeMoyne R, Tomycz N, Mastroianni T, McCandless C, Cozza M, Peduto D (2015) Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning. In: 37th annual international conference of the IEEE, Engineering in Medicine and Biology Society (EMBS), pp 6772–6775Google Scholar
  13. 13.
    LeMoyne R, Mastroianni T, Tomycz N, Whiting D, Oh M, McCandless C, Currivan C, Peduto D (2017) Implementation of a multilayer perceptron neural network for classifying deep brain stimulation in ‘On’ and ‘Off’ modes through a smartphone representing a wearable and wireless sensor application. In: 47th Society for Neuroscience annual meeting (featured in Hot Topics; top 1% of abstracts)Google Scholar
  14. 14.
    LeMoyne R, Mastroianni T, McCandless C, Currivan C, Whiting D, Tomycz N (2018) Implementation of a smartphone as a wearable and wireless accelerometer and gyroscope platform for ascertaining deep brain stimulation treatment efficacy of Parkinson’s disease through machine learning classification. Adv Park Dis 7(2):19–30Google Scholar
  15. 15.
    LeMoyne R, Mastroianni T, Tomycz N, Whiting D, McCandless C, Peduto D, Cozza M (2015) I-Phone wireless accelerometer quantification of extremity tremor in essential tremor patient undergoing activated and inactivated deep brain stimulation. In: International Neuromodulation Society’s 12th World CongressGoogle Scholar
  16. 16.
    LeMoyne R, Mastroianni T (2018) Bluetooth inertial sensors for gait and reflex response quantification with perspectives regarding cloud computing and the Internet of Things. In: Wearable and wireless systems for healthcare I: gait and reflex response quantification. Springer, Singapore, pp 95–103Google Scholar
  17. 17.
    Heldman DA, Harris DA, Felong T, Andrzejewski KL, Dorsey ER, Giuffrida JP, Goldberg B, Burack MA (2017) Telehealth management of Parkinson’s disease using wearable sensors: an exploratory study. Digit Biomark 1(1):43–51Google Scholar
  18. 18.
    Heldman DA, Giuffrida JP, Cubo E (2016) Wearable sensors for advanced therapy referral in Parkinson’s disease. J Park Dis 6(3):631–638CrossRefGoogle Scholar
  19. 19.
    López-Blanco R, Velasco MA, Méndez-Guerrero A, Romero JP, del Castillo MD, Serrano JI, Benito-León J, Bermejo-Pareja F, Rocon E (2018) Essential tremor quantification based on the combined use of a smartphone and a smartwatch: the NetMD study. J Neurosci Methods 303:95–102CrossRefGoogle Scholar
  20. 20.
    Zheng X, Vieira Campos A, Ordieres-Meré J, Balseiro J, Labrador Marcos S, Aladro Y (2017) Continuous monitoring of essential tremor using a portable system based on smartwatch. Front Neurol 8:96CrossRefGoogle Scholar
  21. 21.
    Rovini E, Esposito D, Maremmani C, Bongioanni P, Cavallo F (2014) Using wearable sensor systems for objective assessment of Parkinson’s disease. In: 20th IMEKO TC4 international symposium and 18th international workshop on ADC modelling and testing, pp 862–867Google Scholar
  22. 22.
    Kim HB, Lee WW, Kim A, Lee HJ, Park HY, Jeon HS, Kim SK, Jeon B, Park KS (2018) Wrist sensor-based tremor severity quantification in Parkinson’s disease using convolutional neural network. Comput Biol Med 95:140–146CrossRefGoogle Scholar
  23. 23.
    van den Noort JC, Verhagen R, van Dijk KJ, Veltink PH, Vos MC, de Bie RM, Bour LJ, Heida CT (2017) Quantification of hand motor symptoms in Parkinson’s disease: a proof-of-principle study using inertial and force sensors. Ann Biomed Eng 45(10):2423–2436CrossRefGoogle Scholar
  24. 24.
    Johansson D, Malmgren K, Murphy MA (2018) Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review. J Neurol 265(8):1740–1752CrossRefGoogle Scholar
  25. 25.
    Rovini E, Maremmani C, Cavallo F (2018) Automated systems based on wearable sensors for the management of Parkinson’s disease at home: a systematic review. Telemed E-Health (Epub ahead of print)Google Scholar
  26. 26.
    Pasluosta CF, Gassner H, Winkler J, Klucken J, Eskofier BM (2015) An emerging era in the management of Parkinson’s disease: wearable technologies and the Internet of Things. IEEE J Biomed Health Inform 19(6):1873–1881CrossRefGoogle Scholar
  27. 27.
    LeMoyne R, Mastroianni T (2018) Future perspective of network centric therapy. In: Wearable and wireless systems for healthcare I: gait and reflex response quantification. Springer, Singapore, pp 133–134Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Robert LeMoyne
    • 1
  • Timothy Mastroianni
    • 2
  • Donald Whiting
    • 3
  • Nestor Tomycz
    • 3
  1. 1.Department of Biological Sciences and Center for Bioengineering InnovationNorthern Arizona UniversityFlagstaffUSA
  2. 2.IndependentPittsburghUSA
  3. 3.Department of Neurosurgery Allegheny General HospitalAllegheny Health Network Neuroscience InstitutePittsburghUSA

Personalised recommendations