Eskofier B, Lee SI, Daneault JF, Golabchi FN, Ferreira-Carvalho G, Vergara-Diaz G, Sapienza S, Costante G, Klucken J, Kautz T, Bonato P (2016)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2016
Pages Range: 655-658
Conference Proceedings Title: Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016)
Event location: Orlando, USA
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data.
We collected data from ten patients with idiopathic Parkinson’s disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.
APA:
Eskofier, B., Lee, S.I., Daneault, J.-F., Golabchi, F.N., Ferreira-Carvalho, G., Vergara-Diaz, G.,... Bonato, P. (2016). Recent Machine Learning Advancements in Sensor-Based Mobility Analysis: Deep Learning for Parkinson’s Disease Assessment. In Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016) (pp. 655-658). Orlando, USA.
MLA:
Eskofier, Björn, et al. "Recent Machine Learning Advancements in Sensor-Based Mobility Analysis: Deep Learning for Parkinson’s Disease Assessment." Proceedings of the Proceedings of the 38th IEEE Engineering in Medicine and Biology Society Conference (EMBC 2016), Orlando, USA 2016. 655-658.
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