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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Book ChapterDOI
Tremor Identification Using Machine Learning in Parkinson's Disease
TL;DR: This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.
Journal ArticleDOI
Deep learning approaches for human activity recognition using wearable technology
Book ChapterDOI
Wearable-Based Parkinson's Disease Severity Monitoring Using Deep Learning.
Jann Goschenhofer,Franz M. J. Pfister,Kamer Ali Yuksel,Bernd Bischl,Urban Fietzek,Janek Thomas +5 more
TL;DR: In this article, the authors developed and examined a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device, and they found that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task.
Journal ArticleDOI
An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism
TL;DR: An unsupervised autoencoder feature selection technique is proposed, and passed the compressed features to supervised machine-learning (ML) algorithms, and the state-of-the-art 1D convolutional neural network (CNN-1D) for PD classification is investigated.
Book ChapterDOI
IoT-based location-aware smart healthcare framework with user mobility support in normal and emergency scenario: a comprehensive survey
TL;DR: Functional framework of IoT-based healthcare with the state-of-the-art literature survey has been illustrated, followed by location-aware protocols, learning techniques for intelligent healthcare, and future research directives igniting interests of researchers in this emerging domain are illustrated.
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