Early Diagnosis of Parkinson's disease using a smartphone

Kun-Chan Lan, Wen Yuah Shih

Research output: Contribution to journalConference article

8 Citations (Scopus)

Abstract

Diagnosing Parkinson's disease (PD) is often difficult, especially in its early stages. It has been estimated that nearly 40% of people with the disease may not be diagnosed. Traditionally, the diagnosis of Parkinson's disease often requires a doctor to observe the patient over time to recognize signs of rigidity. In this work, we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smart-phone. Our tool could be useful in providing an early warning to the PD patient to seek medical assistance and help the doctor diagnose the disease earlier.

Original languageEnglish
Pages (from-to)305-312
Number of pages8
JournalProcedia Computer Science
Volume34
DOIs
Publication statusPublished - 2014 Jan 1
Event9th International Conference on Future Networks and Communications, FNC 2014 and the 11th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2014 - Niagara Falls, ON, Canada
Duration: 2014 Aug 172014 Aug 20

Fingerprint

Smartphones
Rigidity

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

@article{6738dc397436416d9469b01dafb5b1ec,
title = "Early Diagnosis of Parkinson's disease using a smartphone",
abstract = "Diagnosing Parkinson's disease (PD) is often difficult, especially in its early stages. It has been estimated that nearly 40{\%} of people with the disease may not be diagnosed. Traditionally, the diagnosis of Parkinson's disease often requires a doctor to observe the patient over time to recognize signs of rigidity. In this work, we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smart-phone. Our tool could be useful in providing an early warning to the PD patient to seek medical assistance and help the doctor diagnose the disease earlier.",
author = "Kun-Chan Lan and Shih, {Wen Yuah}",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.procs.2014.07.028",
language = "English",
volume = "34",
pages = "305--312",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",

}

Early Diagnosis of Parkinson's disease using a smartphone. / Lan, Kun-Chan; Shih, Wen Yuah.

In: Procedia Computer Science, Vol. 34, 01.01.2014, p. 305-312.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Early Diagnosis of Parkinson's disease using a smartphone

AU - Lan, Kun-Chan

AU - Shih, Wen Yuah

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Diagnosing Parkinson's disease (PD) is often difficult, especially in its early stages. It has been estimated that nearly 40% of people with the disease may not be diagnosed. Traditionally, the diagnosis of Parkinson's disease often requires a doctor to observe the patient over time to recognize signs of rigidity. In this work, we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smart-phone. Our tool could be useful in providing an early warning to the PD patient to seek medical assistance and help the doctor diagnose the disease earlier.

AB - Diagnosing Parkinson's disease (PD) is often difficult, especially in its early stages. It has been estimated that nearly 40% of people with the disease may not be diagnosed. Traditionally, the diagnosis of Parkinson's disease often requires a doctor to observe the patient over time to recognize signs of rigidity. In this work, we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smart-phone. Our tool could be useful in providing an early warning to the PD patient to seek medical assistance and help the doctor diagnose the disease earlier.

UR - http://www.scopus.com/inward/record.url?scp=84906821550&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84906821550&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2014.07.028

DO - 10.1016/j.procs.2014.07.028

M3 - Conference article

AN - SCOPUS:84906821550

VL - 34

SP - 305

EP - 312

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

ER -