Proceedings ArticleDOI
Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment
Bjoern M. Eskofier,Sunghoon Ivan Lee,Jean-Francois Daneault,Fatemeh Noushin Golabchi,Gabriela Ferreira-Carvalho,Gloria Vergara-Diaz,Stefano Sapienza,Gianluca Costante,Jochen Klucken,Thomas Kautz,Paolo Bonato +10 more
- Vol. 2016, pp 655-658
TLDR
This paper compared standard machine learning pipelines with deep learning based on convolutional neural networks and showed that deep learning outperformed other state-of-the-art machine learning algorithms in terms of classification rate.Abstract:
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.read more
Citations
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Journal ArticleDOI
Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease
Mohamed Shaban,Amy W. Amara +1 more
TL;DR: In this article , a deep learning approach based on a recently proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication.
Proceedings Article
Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization.
Terry Taewoong Um,Franz M. J. Pfister,Daniel Pichler,Satoshi Endo,Muriel Lang,Sandra Hirche,Urban Fietzek,Dana Kulic +7 more
TL;DR: An automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor and an ensemble of convolutional neural networks is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability.
Posted Content
DeepHealth: Review and challenges of artificial intelligence in health informatics
Gloria Hyunjung Kwak,Pan Hui +1 more
TL;DR: This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research.
Journal ArticleDOI
Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects
TL;DR: A new data analysis approach that can be used along with two wearable IMU (inertial measurement units) sensors to continuously assess motor fluctuations in PD patients while moving in their natural environment is described, showing great promise to continuously detect medication states from continuous monitoring of the subjects’ movement.
Proceedings ArticleDOI
Automated vision-based analysis of levodopa-induced dyskinesia with deep learning
TL;DR: This study presents the first application of deep learning to video analysis in PD, and demonstrates promise for future development of a non-contact system for objective PD assessment.
References
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Adam: A Method for Stochastic Optimization
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TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
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