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

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. F...

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Bibliographic Details
Published in:Electronics (Basel) 2019-02, Vol.8 (2), p.119
Main Authors: San-Segundo, Rubén, Torres-Sánchez, Roque, Hodgins, Jessica, De la Torre, Fernando
Format: Article
Language:English
Subjects:
Accelerometers
Algorithms
Artificial neural networks
Classification
Datasets
Decision trees
Evaluation
Experiments
Gait
Markov chains
Multilayer perceptrons
Neural networks
Parkinson's disease
Patients
Principal components analysis
Quality assessment
Robustness
Sensors
Signal processing
Speech processing
Supervision
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics8020119