PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

References

1. 1)
  - 8. Samà, A., Pérez-López, C., Rodríguez-Martín, D., et al: ‘A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system’, Innov. Med. Healthcare, 2015, 207, pp. 115–124.
2. 2)
  - 3. ‘Kinesia, Objective motor assessment’, http://www.glneurotech.com, accessed 13 January 2017.
3. 3)
  - 19. Goetz, C.G., Tilley, B.C., Shaftman, S.R., et al: ‘Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results’, Mov. Disord., 2008, 23, (15), pp. 2129–2170 (doi: 10.1002/mds.22340).
4. 4)
  - 10. ‘3dnet’, http://www.3dnetmedical.com, accessed 13 January 2017.
5. 5)
  - 14. Robbins, T.W., James, M., Owen, A.M., et al: ‘Cambridge neuropsychological test automated battery: a factor analytic study of a large sample of normal elderly volunteers’, Dementia, 1994, 5, pp. 266–281.
6. 6)
  - 20. Rigas, G., Gatsios, D., Fotiadis, D.I., et al: ‘Tremor UPDRS estimation in home environment’. 38th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, Orlando, 2016, pp. 3642–3645.
7. 7)
  - 13. ‘Vaica – Medication Adherence Customised Solutions’, http://www.vaica.com, accessed 13 January 2017.
8. 8)
  - 17. Martinez-Martin, P., Rodriguez-Blazquez, C., Abe, K., et al: ‘International study on the psychometric attributes of the non-motor symptoms scale in Parkinson disease’, Neurology, 2009, 73, (19), pp. 1584–1591 (doi: 10.1212/WNL.0b013e3181c0d416).
9. 9)
  - 6. Bot, B.M., Suver, C., Neto, E.C., et al: ‘The mPower study, Parkinson disease mobile data collected using ResearchKit’, Scientific Data, 2016, 3, p. 160011 (doi: 10.1038/sdata.2016.11).
10. 10)
  - 9. ‘SENSE-PARK’, http://www.sense-park.eu, accessed 13 January 2017.
11. 11)
  - 21. Bohanec, M., Znidarsic, M., Rajkovic, V., et al: ‘DEX methodology: three decades of qualitative multi-attribute modeling’, Informatica, 2013, 37, (1), pp. 49–54.
12. 12)
  - 5. ‘myHealth Pal’, http://www.myhealthpal.com, accessed 13 January 2017.
13. 13)
  - 15. Tsanas, A., Little, M.A., McSharry, P.E., et al: ‘Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease’, IEEE Trans. Biomed. Eng., 2012, 59, (5), pp. 1264–1271 (doi: 10.1109/TBME.2012.2183367).
14. 14)
  - 18. Stanford, M.S., Mathias, C.W., Dougherty, D.M., et al: ‘Fifty years of the Barratt impulsiveness scale: an update and review’, Pers. Individ. Differ., 2009, 47, (5), pp. 385–395 (doi: 10.1016/j.paid.2009.04.008).
15. 15)
  - 4. ‘Parkinson's Toolkit, National Parkinson Foundation’, http://www.toolkit.parkinson.org, accessed 13 January 2017.
16. 16)
  - 16. Gurinović, M., Milešević, J., Novaković, R., et al: ‘Improving nutrition surveillance and public health research in Central and Eastern Europe/Balkan Countries using the Balkan Food Platform and dietary tools’, Food Chem., 2016, 193, pp. 173–180 (doi: 10.1016/j.foodchem.2015.03.103).
17. 17)
  - 12. ‘Moticon’ http://www.moticon.de, accessed 13 January 2017.
18. 18)
  - 2. ‘PKG Data Logger’, http://www.globalkineticscorporation.com, accessed 13th January 2017.
19. 19)
  - 11. ‘Microsoft Band|Official Site’ http://www.microsoft.com/Microsoft-Band, accessed 13 January 2017.
20. 20)
  - 1. Dorsey, E.R., Constantinescu, R., Thompson, J.P., et al: ‘Projected number of people with Parkinson's disease in the most populous nations, 2005 through 2030’, Neurology, 2007, 68, (5), pp. 384–386 (doi: 10.1212/01.wnl.0000247740.47667.03).
21. 21)
  - 7. Tzallas, A.T., Tsipouras, M.G., Rigas, G., et al: ‘PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease’, Sensors, 2014, 14, (11), pp. 21329–21357 (doi: 10.3390/s141121329).

PD_Manager: an mHealth platform for Parkinson's disease patient management

References

Related content