Automated Tremor Detection in Parkinson's Disease Using Accelerometer Signals

Ada Zhang	Rubén San-Segundo	Stanislav Panev	Griffin Tabor	Katelyn Stebbins	Andrew Whitford	Fernando De la Torre	Jessica Hodgins

Proceedings of the 27th IEEE International Symposium (2018)

Wearable sensor technology has the potential to transform the treatment of Parkinson’s Disease (PD) by providing objective analysis about the frequency and severity of symptoms in everyday life. However, many challenges remain to developing a system that can robustly distinguish PD motor symptoms from normal motion. Stronger feature sets may help to improve the detection accuracy of such a system. In this work, we explore several feature sets compared across two classification algorithms for PD tremor detection. We find that features automatically learned by a Convolutional Neural Network (CNN) lead to the best performance, although our handcrafted features are close behind. We also find that CNNs benefit from training on data decomposed into tremor and activity spectra as opposed to raw data

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Ada Zhang, Rubén San-Segundo, Stanislav Panev, Griffin Tabor, Katelyn Stebbins, Andrew Whitford, Fernando De la Torre, Jessica Hodgins (2018). Automated Tremor Detection in Parkinson's Disease Using Accelerometer Signals. Proceedings of the 27th IEEE International Symposium, 13-14.

@article{
author = {Ada Zhang, Rubén San-Segundo, Stanislav Panev, Griffin Tabor, Katelyn Stebbins, Andrew Whitford, Fernando De la Torre, Jessica Hodgins},
title = {Automated Tremor Detection in Parkinson's Disease Using Accelerometer Signals},
journal = {Proceedings of the 27th IEEE International Symposium},
issue_date = {September 2018},
month = Sept,
year = {2018},
pages = {13-14},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8648666},
publisher = {IEEE},