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
An Essay on the Shaking Palsy
TL;DR: In this paper, the authors present a conciliatory explanation for the present publication, in which, it is acknowledged, that mere conjecture takes the place of experiment; and, that analogy is the substitute for anatomical examination, the only sure foundation for pathological knowledge.
Journal ArticleDOI
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
TL;DR: The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition, and categorise the studies into generative, discriminative and hybrid methods.
Proceedings ArticleDOI
Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
Terry Taewoong Um,Franz M. J. Pfister,Daniel Pichler,Satoshi Endo,Muriel Lang,Sandra Hirche,Urban Fietzek,Dana Kulic +7 more
TL;DR: The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability.
Journal ArticleDOI
How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.
TL;DR: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases), and home and long-term monitoring.
Proceedings ArticleDOI
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
Terry Taewoong Um,Franz M. J. Pfister,Daniel Pichler,Satoshi Endo,Muriel Lang,Sandra Hirche,Urban Fietzek,Dana Kulic +7 more
TL;DR: In this article, various data augmentation methods for wearable sensor data are proposed and applied to the classification of the motor state of Parkinson's disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability Appropriate augmentation improves the classification performance from 7754% to 8688%
References
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Proceedings ArticleDOI
Return of the Devil in the Details: Delving Deep into Convolutional Nets
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Proceedings Article
A Theoretical Analysis of Feature Pooling in Visual Recognition
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Journal ArticleDOI
An Essay on the Shaking Palsy
TL;DR: In this paper, the authors present a conciliatory explanation for the present publication, in which, it is acknowledged, that mere conjecture takes the place of experiment; and, that analogy is the substitute for anatomical examination, the only sure foundation for pathological knowledge.
Journal ArticleDOI
Unobtrusive Sensing and Wearable Devices for Health Informatics
Yali Zheng,Xiaorong Ding,Carmen C. Y. Poon,Benny Lo,Heye Zhang,Xiao-Lin Zhou,Guang-Zhong Yang,Ni Zhao,Yuan-Ting Zhang +8 more
TL;DR: This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobTrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion.
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