title: Telemonitoring of patients with Parkinson's disease using inertia sensors.
abstract: 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.
  
  
  OBJECTIVE
  To allow for the objective evaluation of patients' symptoms required for optimal dosefinding, 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.
  
  
  CONCLUSIONS
  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 interoperability and usability were identified and realized and remain to be tested in the field.
authors.affiliation: Institute of Medical Engineering, University of Applied Science Ulm.
authors.forename: N E
authors.initials: NE
authors.lastname: Piro
authors.affiliation: Institute of Medical Engineering, University of Applied Science Ulm.
authors.forename: L
authors.initials: L
authors.lastname: Baumann
authors.affiliation: Institute of Medical Engineering, University of Applied Science Ulm.
authors.forename: M
authors.initials: M
authors.lastname: Tengler
authors.affiliation: Faculty of Mathematics, Ludwig-Maximilians-University Munich.
authors.forename: L
authors.initials: L
authors.lastname: Piro
authors.affiliation: Faculty of Mathematics, Ludwig-Maximilians-University Munich.
authors.forename: R
authors.initials: R
authors.lastname: Blechschmidt-Trapp
mesh_terms: D000054:Acceleration; D000465:Algorithms; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D010300:Parkinson Disease / Q000503:physiopathology* / Q000523:psychology; D010342:Patient Acceptance of Health Care; D012984:Software; D013223:Statistics as Topic; D017216:Telemedicine / Q000295:instrumentation*
keywords: Parkinson’s disease; acceleration; medical informatics; rotation; telehealth