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
A deep explainable artificial intelligent framework for neurological disorders discrimination.
TL;DR: In this article, the authors proposed a data-driven NN model, which processes the kinematics of the hand in the affected individuals and classifies the patients into Parkinson's disease or essential tremor.
Journal ArticleDOI
Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson’s disease patients
Alejandro Garza-Rodríguez,Luis P. Sánchez-Fernández,Luis Alejandro Sánchez-Pérez,José Juan Carbajal Hernández +3 more
TL;DR: The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.
Journal ArticleDOI
Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model
TL;DR: An improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification and the proposed model can be employed for the earlier identification of PD.
Book ChapterDOI
Machine and Deep Learning Algorithms for Wearable Health Monitoring
TL;DR: The advantages of the DL-based approaches over the traditional ML methods were analyzed in line with metrics associated with data feature extraction and identification performances and future research trends required to improve the capability of DL algorithms further are offered.
Journal ArticleDOI
Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life
TL;DR: The proposed gait analysis method confirmed high classification accuracy and the statistical significance of gait factors that can be used for osteopenia and sarcopenia management.
References
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Adam: A Method for Stochastic Optimization
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