Assessment of Tremor Activity in the Parkinson's Disease Using a Set of Wearable Sensors

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle...

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Published inIEEE transactions on information technology in biomedicine Vol. 16; no. 3; pp. 478 - 487
Main Authors Rigas, G., Tzallas, A. T., Tsipouras, M. G., Bougia, P., Tripoliti, E. E., Baga, D., Fotiadis, D. I., Tsouli, S. G., Konitsiotis, S.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.05.2012
Subjects
Accelerometers
Aged
Algorithms
Case-Control Studies
Feature extraction
Hidden Markov models
Hidden Markov models (HMMs)
Humans
Legged locomotion
Levodopa-induced dyskinesia (LID)
Markov Chains
Middle Aged
Monitoring, Ambulatory - instrumentation
Monitoring, Ambulatory - methods
Movement - physiology
Parkinson Disease - physiopathology
Parkinson's disease
Parkinson's disease (PD)
Posture - physiology
posture recognition
Sensor phenomena and characterization
tremor
Tremor - classification
Tremor - diagnosis
Tremor - physiopathology
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Summary:Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.
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ISSN:1089-7771
1558-0032
1558-0032
DOI:10.1109/TITB.2011.2182616