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
Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study.
Milla Juutinen,Cassia Wang,Justin Zhu,Juan Haladjian,Jari Ruokolainen,Juha Puustinen,Juha Puustinen,Antti Vehkaoja +7 more
TL;DR: This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test and shows the differences in feature selection methods and classifiers.
Journal ArticleDOI
A Partially Binarized Hybrid Neural Network System for Low-Power and Resource Constrained Human Activity Recognition
Antonio De Vita,Alessandro Russo,Danilo Pau,Luigi Di Benedetto,Alfredo Rubino,Gian Domenico Licciardo +5 more
TL;DR: A custom Human Activity Recognition system is presented based on the resource-constrained Hardware (HW) implementation of a new partially binarized Hybrid Neural Network, which achieves much higher accuracy than Binarized Neural Network.
Journal ArticleDOI
Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson's Disease Detection.
TL;DR: In this paper, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect Parkinson's disease patients based on their vocal features.
DeepHealth: Deep Learning for Health Informatics
Gloria Hyunjung Kwak,Pan Hui +1 more
TL;DR: This article presents a comprehensive review of research applying deep learning in health informatics with a focus on the last five 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
Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease.
Jean-Francois Daneault,Jean-Francois Daneault,Gloria Vergara-Diaz,Federico Parisi,Chen Admati,Christina Alfonso,Matilde Bertoli,Edoardo Bonizzoni,Gabriela Ferreira Carvalho,Gianluca Costante,Eric Fabara,Naama Fixler,Fatemah Noushin Golabchi,John H. Growdon,Stefano Sapienza,Phil Snyder,Shahar Shpigelman,Lewis Sudarsky,Margaret Daeschler,Lauren Bataille,Solveig K. Sieberts,Larsson Omberg,Steven T. Moore,Steven T. Moore,Paolo Bonato,Paolo Bonato +25 more
TL;DR: The Levodopa Response Study as discussed by the authors used wearable accelerometers and waist-worn smartphones to assess motor symptom severity in individuals with Parkinson's disease (PD) exhibiting motor fluctuations, including tremor, bradykinesia, and rigidity.
References
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Adam: A Method for Stochastic Optimization
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