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Article

Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease

1
Information Processing and Telecommunications Center, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
2
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3
Dpto de Ingeniería de Sistemas y Automática, Escuela Técnica Superior de Ingeniería Industrial, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(2), 119; https://doi.org/10.3390/electronics8020119
Received: 19 December 2018 / Revised: 14 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
(This article belongs to the Section Bioelectronics)
This paper focuses on detecting freezing of gait in Parkinson’s patients using body-worn accelerometers. In this study, we analyzed the robustness of four feature sets, two of which are new features adapted from speech processing: mel frequency cepstral coefficients and quality assessment metrics. For classification based on these features, we compared random forest, multilayer perceptron, hidden Markov models, and deep neural networks. These algorithms were evaluated using a leave-one-subject-out (LOSO) cross validation to match the situation where a system is being constructed for patients for whom there is no training data. This evaluation was performed using the Daphnet dataset, which includes recordings from ten patients using three accelerometers situated on the ankle, knee, and lower back. We obtained a reduction from 17.3% to 12.5% of the equal error rate compared to the previous best results using this dataset and LOSO testing. For high levels of sensitivity (such as 0.95), the specificity increased from 0.63 to 0.75. The biggest improvement across all of the feature sets and algorithms tested in this study was obtained by integrating information from longer periods of time in a deep neural network with convolutional layers. View Full-Text
Keywords: Parkinson’s disease; freezing of gait; mel frequency cepstral coefficients; MFCCs; robust detection; deep learning; convolutional neural networks; CNNs; consecutive windows Parkinson’s disease; freezing of gait; mel frequency cepstral coefficients; MFCCs; robust detection; deep learning; convolutional neural networks; CNNs; consecutive windows
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MDPI and ACS Style

San-Segundo, R.; Navarro-Hellín, H.; Torres-Sánchez, R.; Hodgins, J.; De la Torre, F. Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease. Electronics 2019, 8, 119. https://doi.org/10.3390/electronics8020119

AMA Style

San-Segundo R, Navarro-Hellín H, Torres-Sánchez R, Hodgins J, De la Torre F. Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease. Electronics. 2019; 8(2):119. https://doi.org/10.3390/electronics8020119

Chicago/Turabian Style

San-Segundo, Rubén, Honorio Navarro-Hellín, Roque Torres-Sánchez, Jessica Hodgins, and Fernando De la Torre. 2019. "Increasing Robustness in the Detection of Freezing of Gait in Parkinson’s Disease" Electronics 8, no. 2: 119. https://doi.org/10.3390/electronics8020119

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