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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 939-945
Yiqiang Chen , Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Xiaodong Yang , Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Biao Chen , Beijing Key Laboratory for Parkinson's Disease, Beijing, China
Chunyan Miao , Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Hanchao Yu , Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
ABSTRACT
In clinical settings, the assessment of Parkinson's disease (PD) mainly depends on the doctor's experience and observation. Such assessment often lacks of unified standards and may result in varied diagnoses among different doctors. To cope with this problem, we propose an objective and quantified symptom assessment tool of PD on mobile devices, based on the Unified Parkinson's Disease Rating Scale (UPDRS). The mobile PD assessment tool, PdAssit, assesses PD symptoms by actively delivering six tasks and generates scores equivalent to UPDRS using machines learning models. PdAssit is applied in three essential applications, including medication response detection, symptom self-tracking and diagnostic assistance. The feasibility and effectiveness of PdAssit is demonstrated through clinical experiments.
INDEX TERMS
Feature extraction, Parkinson's disease, Sensors, Accelerometers, Time-domain analysis, Tools
CITATION

Y. Chen, X. Yang, B. Chen, C. Miao and H. Yu, "PdAssist: Objective and quantified symptom assessment of Parkinson's disease via smartphone," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 939-945.
doi:10.1109/BIBM.2017.8217783
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