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SPARK: Personalized Parkinson Disease Interventions through Synergy between a Smartphone and a Smartwatch

  • Vinod Sharma
  • Kunal Mankodiya
  • Fernando De La Torre
  • Ada Zhang
  • Neal Ryan
  • Thanh G. N. Ton
  • Rajeev Gandhi
  • Samay Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8519)

Abstract

Parkinson disease (PD) is a neurodegenerative disorder afflicting more than 1 million aging Americans, incurring $23 billion in annual medical costs in the U.S. alone. Approximately 90% Parkinson patients undergoing treatment have mobility related problems related to medication which prevent them doing their activities of daily living. Efficient management of PD requires complex medication regimens specifically titrated to individuals’ needs. These personalized regimens are difficult to maintain for the patient and difficult to prescribe for a physician in the few minutes available during office visits. Diverging from current form of laboratory-ridden wearable sensor technologies, we have developed SPARK, a framework that leverages a synergistic combination of Smartphone and Smartwatch in monitoring multidimensional symptoms – such as facial tremors, dysfunctional speech, limb dyskinesia, and gait abnormalities. In addition, SPARK allows physicians to conduct effective tele-interventions on PD patients when they are in non-clinical settings (e.g., at home or work). Initial case series that use SPARK framework show promising results of monitoring multidimensional PD symptoms and provide a glimpse of its potential use in real-world, personalized PD interventions.

Keywords

mHealth Smartphone Parkinson Disease Pervasive Healthcare Personalized Health Telemedicine 

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References

  1. 1.
    Dorsey, E.R., Constantinescu, R., Thompson, J.P., Biglan, K.M., Holloway, R.G., Kieburtz, K., Marshall, F.J., Ravina, B.M., Schifitto, G., Siderowf, A., Tanner, C.M.: Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68, 384–386 (2007)CrossRefGoogle Scholar
  2. 2.
    Weintraub, D., Comella, C,L., Horn, S.: Parkinson’s disease–Part 1: Pathophysiology, symptoms, burden, diagnosis, and assessment. The American Journal of Managed Care 2008;14:S40-8. Google Scholar
  3. 3.
    Fahn, S., Elton, R.L., UPDRS Development Committee: Unified Parkinson’s Disease Rating Scale. In: Fahn, S., Marsden, C.D., Calne, D.B., Goldstein, M. (eds.) Recent Developments in Parkinson’s Disease, pp. 153–163. Macmillan, Florham Park (1987)Google Scholar
  4. 4.
    Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease. The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Movement Disorders: Official Journal of the Movement Disorder Society 18(7), 738 (2003)Google Scholar
  5. 5.
    Common Data Elements, Unified Parkinson’s Disease Rating Scale. National Institute of Neurological Disorders and Stroke (NINDS)Google Scholar
  6. 6.
    Maetzler, W., Domingos, J., Srulijes, K., Ferreira, J.J., Bloem, B.R.: Quantitative wearable sensors for objective assessment of Parkinson’s disease. Mov. Disord. 28, 1628–1637 (2013)CrossRefGoogle Scholar
  7. 7.
    Tsipouras, M.G., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S.: On automated assessment of Levodopa- induced dyskinesia in Parkinson’s disease. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference, pp. 2679–2682 (2011)Google Scholar
  8. 8.
    Pavel, P., Hayes, T., Tsay, I., Erdogmus, D., Paul, A., Larimer, N., Jimison, H., Nutt, J.: Continuous Assessment of Gait Velocity in Parkinson’s Disease from Unobtrusive Measurements. In: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering 2007, Kohala Coast, Hawaii, USA, May 2-5 (2007)Google Scholar
  9. 9.
    Keijsers, N.L.W., Horstink, M.W.I.M., Gielen, S.C.A.M.: Ambulatory motor assessment in Parkinson’s disease. Movement Disord. 21, 34–44 (2006)CrossRefGoogle Scholar
  10. 10.
    Moore, S.T., MacDougall, H.G., Ondo, W.G.: Ambulatory monitoring of freezing of gait in Parkinson’s disease. Journal of Neuroscience Methods 167, 340–348 (2008)CrossRefGoogle Scholar
  11. 11.
    Das, S., Amoedo, B., De la Torre, F., Hodgins, J.: Detecting Parkinsons’ symptoms in uncontrolled home environments: a multiple instance learning approach. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference, pp. 3688–3691 (2012)Google Scholar
  12. 12.
    Bonato, P., Sherrill, D.M., Standaert, D.G., Salles, S.S., Akay, M.: Data mining techniques to detect motor fluctuations in Parkinson’s disease. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference, vol. 7, pp. 4766–4769 (2004)Google Scholar
  13. 13.
    Keijsers, N.L., Horstink, M.W., Gielen, S.C.: Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks. Mov. Disord. 18, 70–80 (2003)CrossRefGoogle Scholar
  14. 14.
    Moore, S.T., MacDougall, H.G., Gracies, J.M., Cohen, H.S., Ondo, W.G.: Long-term monitoring of gait in Parkinson’s disease. Gait & Posture 26, 200–207 (2007)CrossRefGoogle Scholar
  15. 15.
    Bachlin, M., Plotnik, M., Roggen, D., Giladi, N., Hausdorff, J.M., Troster, G.: A wearable system to assist walking of Parkinson’s disease patients. Methods of Information in Medicine 49, 88–95 (2010)Google Scholar
  16. 16.
    Patel, S., Lorincz, K., Hughes, R., Huggins, N., Growdon, J., Standaert, D., Akay, M., Dy, J., Welsh, M., Bonato, P.: Monitoring motor fluctuations in patients with Parkinson’s disease using wearable sensors. IEEE Transactions on Information Technology in Biomedicine: A Publication of the IEEE Engineering in Medicine and Biology Society 13, 864–873 (2009)CrossRefGoogle Scholar
  17. 17.
    Patel, S., Chen, B.R., Mancinelli, C., Paganoni, S., Shih, L., Welsh, M., Dy, J., Bonato, P.: Longitudinal monitoring of patients with Parkinson’s disease via wearable sensor technology in the home setting. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference 2011, pp. 1552–1555 (2011)Google Scholar
  18. 18.
    Zabaleta, H., Keller, T., Fimbel, E.: Gait analysis in frequency domain for freezing detection in patients with Parkinson’s disease. Gerontechnology 7 (2008)Google Scholar
  19. 19.
    Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J.P., Giladi, N., Hausdorff, J.M.: Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer. Neurorehabilitation and Neural Repair 25, 810–818 (2011)CrossRefGoogle Scholar
  20. 20.
    Griffiths, R.I., Kotschet, K., Arfon, S., Xu, Z.M., Johnson, W., Drago, J., Evans, A., Kempster, P., Raghav, S., Horne, M.K.: Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease. Journal of Parkinson’s Disease 2, 47–55 (2012)Google Scholar
  21. 21.
    Mera, T.O., Heldman, D.A., Espay, A.J., Payne, M., Giuffrida, J.P.: Feasibility of home-based automated Parkinson’s disease motor assessment. Journal of Neuroscience Methods 203, 152–156 (2012)CrossRefGoogle Scholar
  22. 22.
    Zwartjes, D., Heida, T., van Vugt, J., Geelen, J., Veltink, P.: Ambulatory Monitoring of Activities and Motor Symptoms in Parkinson inverted question marks Disease. IEEE Transactions on Bio-medical Engineering 57 (2010)Google Scholar
  23. 23.
    Madeley, P., Ellis, A.W., Mindham, R.H.S.: Facial expressions and Parkinson’s disease. Behavioural Neurology 8(2), 115–119 (1995)CrossRefGoogle Scholar
  24. 24.
    Howard, N., Bergmann, J.H.M., Howard, R.: Examining Everyday Speech and Motor Symptoms of Parkinson’s Disea‘se for Diagnosis and Progression Tracking. In: 2013 12th Mexican International Conference on Artificial Intelligence (MICAI). IEEE (2013)Google Scholar
  25. 25.
    Jankovic, J., McDermott, M., Carter, J., Gauthier, S., Goetz, C., Golbe, L., Huber, S., Koller, W., Olanow, C., Shoulson, I., et al.: Variable expression of Parkinson’s disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology 40, 1529–1534 (1990)Google Scholar
  26. 26.
    Pebble Data Logging Guide, http://developer.getpebble.com/2/guides/datalogging-guide.html (accessed on February 13, 2014)
  27. 27.
    Mankodiya, K., Sharma, V., Martins, R., Pande, I., Jain, S., Ryan, N., Gandhi, R.: Understanding User’s Emotional Engagement to the Contents on a Smartphone Display: Psychiatric Prospective. In: 2013 IEEE 10th International Conference on and 10th International Conference on Ubiquitous Intelligence and Computing Autonomic and Trusted Computing (UIC/ATC), December 18-21, pp. 631–637 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vinod Sharma
    • 1
  • Kunal Mankodiya
    • 3
  • Fernando De La Torre
    • 4
  • Ada Zhang
    • 4
  • Neal Ryan
    • 1
  • Thanh G. N. Ton
    • 5
  • Rajeev Gandhi
    • 3
  • Samay Jain
    • 2
  1. 1.Dept. of PsychiatryUniversity of PittsburghPittsburghUSA
  2. 2.Dept. of NeurologyUniversity of PittsburghPittsburghUSA
  3. 3.Dept. of Electrical & Computer EngineeringCarnegie Mellon UniversityPittsburghUSA
  4. 4.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  5. 5.Dept. of NeurologyUniversity of WashingtonSeattleUSA

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