PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.
In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, P.O. Box 1186, GR 45110 Ioannina, Greece. atzallas@cc.uoi.gr.
Alexandros T
AT
Tzallas
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, P.O. Box 1186, GR 45110 Ioannina, Greece. markos@cs.uoi.gr.
Markos G
MG
Tsipouras
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, P.O. Box 1186, GR 45110 Ioannina, Greece. rigas@cs.uoi.gr.
Georgios
G
Rigas
Dept. of Informatics and Telecommunication Engineering, University of Western Macedonia, GR 50100 Kozani, Greece. dtsalikakis@uowm.gr.
Dimitrios G
DG
Tsalikakis
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, P.O. Box 1186, GR 45110 Ioannina, Greece. ekarvuni@cc.uoi.gr.
Evaggelos C
EC
Karvounis
Dept. of Neurology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece. mariachond@gmail.com.
Maria
M
Chondrogiorgi
ANCO S.A. Research and Development Division, 44, Syngrou Avenues, Athens 11742, Greece. fpso94@otenet.gr.
Fotis
F
Psomadellis
Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid 28040, Spain. jcancela@lst.tfo.upm.es.
Jorge
J
Cancela
Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid 28040, Spain. mpastorino@lst.tfo.upm.es.
Matteo
M
Pastorino
Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid 28040, Spain. mta@lst.tfo.upm.es.
María Teresa Arredondo
MT
Waldmeyer
Dept. of Neurology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece. skonitso@uoi.gr.
Spiros
S
Konitsiotis
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, P.O. Box 1186, GR 45110 Ioannina, Greece. fotiadis@cc.uoi.gr.
Dimitrios I
DI
Fotiadis
D056044:Actigraphy / Q000295:instrumentation*; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation; D004360:Drug Therapy, Computer-Assisted / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010300:Parkinson Disease / Q000175:diagnosis* / Q000628:therapy*; D017010:Reminder Systems / Q000295:instrumentation*; D018511:Systems Integration; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods; D013813:Therapy, Computer-Assisted / Q000295:instrumentation