Telemonitoring of patients with Parkinson’s disease using inertia sensors

Summary

Background: Medical treatment in patients suffering from Parkinson’s disease is very difficult as dose-finding is mainly based on selective and subjective impressions by the physician.

Objectives: To allow for the objective evaluation of patients’ symptoms required for optimal dose-finding, a telemonitoring system tracks the motion of patients in their surroundings. The system focuses on providing interoperability and usability in order to ensure high acceptance.

Methods: Patients wear inertia sensors and perform standardized motor tasks. Data are recorded, processed and then presented to the physician in a 3D animated form. In addition, the same data is rated based on the UPDRS score. Interoperability is realized by developing the system in compliance with the recommendations of the Continua Health Alliance. Detailed requirements analysis and continuous collaboration with respective user groups help to achieve high usability.

Results: A sensor platform was developed that is capable of measuring acceleration and angular rate of motions as well as the absolute orientation of the device itself through an included compass sensor. The system architecture was designed and required infrastructure, and essential parts of the communication between the system components were implemented following Continua guidelines. Moreover, preliminary data analysis based on three-dimensional acceleration and angular rate data could be established.

Conclusion: A prototype system for the telemonitoring of Parkinson’s disease patients was successfully developed. The developed sensor platform fully satisfies the needs of monitoring patients of Parkinson’s disease and is comparable to other sensor platforms, although these sensor platforms have yet to be tested rigorously against each other. Suitable approaches to provide interoper-ability and usability were identified and realized and remain to be tested in the field.

Citation: Piro NE, Baumann L, Tengler M, Piro L, Blechschmidt-Trapp R. Telemonitoring of patients with Parkinson’s disease using inertia sensors. Appl Clin Inf 2014; 5: 503–511 http://dx.doi.org/10.4338/ACI-04-RA-0046

Keywords

Parkinson’s disease - telehealth - medical informatics - acceleration - rotation

• References

• 1 Hacke W. Neurologie. 13th ed. Heidelberg: Springer; 2010
• 2 Jimenez-Fernandez S, de Toledo P, del Pozo F. Usability and Interoperability in Wireless Sensor Networks for Patient Telemonitoring in Chronic Disease Management. IEEE Trans Biomed Eng 2013 Sep 5 60 (12) 3331-3339.
• 3 Patel S, Chen BR, Buckley T, Rednic R, McClure D, Tarsy D, Shih L, Dy J, Welsh M, Bonato P. Home monitoring of patients with Parkinson’s disease via wearable technology and a web-based application. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 4411-4414.
• 4 Herrlich S, Spieth S, Nouna R, Zengerle R, Giannola LI, Pardo-Ayala DE, Federico E, Garino P. Ambulatory Treatment and Telemonitoring of Patients with Parkinson’s Disease. In: Wichert R, Eberhardt B, editors. Ambient Assisted Living: Proceedings of the 4th AAL-Congress 2011. January 25–26 Berlin, Germany. Berlin: Springer; 2011. P 295-305
• 5 Pulliam CL, Eichenseer SR, Goetz CG, Waln O, Hunter CB, Jankovic J. et al. Continuous in-home monitoring of essential tremor. Parkinsonism Relat Disord 2014; Jan 20 (Suppl. 01) 37-40
• 6 Moore ST, Yungher DA, Morris TR, Dilda V, MacDougall HG, Shine JM, Naismith SL, Lewis SJ. Autonomous identification of freezing of gait in Parkinson’s disease from lower-body segmental accelerometry. J Neuroeng Rehabil 2013; 10: 19.
• 7 Heldman DA, Filipkowski DE, Riley DE, Whitney CM, Walter BL, Gunzler SA, Giuffrida JP, Mera TO. Automated motion sensor quantification of gait and lower extremity bradykinesia. Conf Proc IEEE Eng Med Biol Soc 2012 2012; 1956-1956.
• 8 IEEE.. IEEE Standards Glossary. New York: IEEE; c2014 [cited 2014 Mar 28]. Available from: http://www. ieee.org/education_careers/education/standards/standards_glossary.html.
• 9 HIMSS.. Definition of Interoperability. Chicago: HIMSS; c2012–14 [cited 2014 Mar 28]. Available from: http://www.himss.org/library/interoperability-standards/what-is?navItemNumber=17333.
• 10 Continua Health Alliance.. Continua Design Guidelines Version 2012. Beaverton (OR): Continua Health Alliance; c2013 [cited 2014 Mar 28] Available from: http://www.continuaalliance.org.
• 11 Madgwick SOH, Harrison AJL, Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE Int Conf Rehabil Robot 2011; 2011: 5975346.
• 12 Kalkbrenner C, Hacker S, Algorri ME, Blechschmidt-Trapp R. Motion capturing with Inertial Measurement Units and Kinect Tracking of limb movement using optical and orientation information. Proceedings of the 7th International Conference on Biomedical Electronics and Devices: Biodevices 2014 2014 Mar 03–06 Angers, France; p. 120-126
• 13 Hacker S, Kalkbrenner C, Algorri ME, Blechschmidt-Trapp R. Gait Analysis with IMU Gaining new Orientation Information of the Lower Leg. Proceedings of the 7th International Conference on Biomedical Electronics and Devices: Biodevices 2014 2014 Mar 03–06 Angers, France; p. 127-133.
• 14 Chen BR, Patel S, Buckley T, Rednic R, McClure DJ, Shih L, Tarsy D, Welsh M, Bonato P. A Web-Based System for Home Monitoring of Patients With Parkinson Disease Using Wearable Sensors. IEEE Trans Biomed Eng 2011; 58 (Suppl. 03) 831-836.