A Classification System for Assessment and Home Monitoring of Tremor in Patients with Parkinson’s Disease

Omid Bazgir, Seyed Amir Hassan Habibi, Lorenzo Palma, Paola Pierleoni, Saba Nafees

DOI: 10.4103/jmss.JMSS_50_17


Tremor is one of the most common symptoms of Parkinson’s disease (PD), which is widely being
used in the diagnosis procedure. Accurate estimation of PD tremor based on Unifed PD Rating Scale
(UPDRS) provides aid for physicians in prescription and home monitoring. This article presents a
robust design of a classification system to estimate PD patient’s hand tremors and the results of the
proposed system as compared to the UPDRS. A smartphone accelerometer sensor is used for accurate
and noninvasive data acquisition. We applied short‑time Fourier transform to time series data of 52
PD patients. Features were extracted based on the severity of PD patients’ hand tremor. The wrapper
method was employed to determine the most discriminative subset of the extracted features. Four
different classifiers were implemented for achieving best possible accuracy in the estimation of PD
hand tremor based on UPDRS. Of the four tested classifers, the Naive Bayesian approach proved to
be the most accurate one. The classification result for the assessment of PD tremor achieved close to
100% accuracy by selecting an optimum combination of extracted features of the acceleration signal
acquired. For home health‑care monitoring, the proposed algorithm was also implemented on a
cost‑effective embedded system equipped with a microcontroller, and the implemented classification
algorithm achieved 93.8% average accuracy. The accuracy result of both implemented systems on
MATLAB and microcontroller is acceptable in comparison with the previous works.


Classifcation, home monitoring, Parkinson’s tremor, smartphone, supervised learning, Unifed Parkinson’s Disease Rating Scale

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