Classification of Activities of Daily Living in Subjects with Parkinson's Disease using Artificial Neural Networks
Parkinson's disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable information to assess the quality of movement in patients with PD [2]. The present work proposes improving the follow-up and support system...
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| Veröffentlicht in: | Pan American Health Care Exchanges (Online) S. 1 - 5 |
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IEEE
27.03.2023
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| ISSN: | 2327-817X |
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| Abstract | Parkinson's disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable information to assess the quality of movement in patients with PD [2]. The present work proposes improving the follow-up and support systems issued by specialists in patients with PD by identifying and adequately classifying the activities of daily living (ADL) carried out by these patients by applying programmed artificial neuronal networks (ANN) in Python language. The proposed supervised learning method allowed the classification of 6 ADLs through 64 signals from inertial sensors that made up the database (dataset) to train a multilayer perceptron (MLP), achieving an accuracy of 99%. Also, by applying dimensionality reduction to dataset, it was possible to reach 80% of accuracy in the classification. These results can potentially help the specialist to reduce costs and achieve a more accurate evaluation, seeking to improve monitoring and quality in the rehabilitation of these patients. |
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| AbstractList | Parkinson's disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable information to assess the quality of movement in patients with PD [2]. The present work proposes improving the follow-up and support systems issued by specialists in patients with PD by identifying and adequately classifying the activities of daily living (ADL) carried out by these patients by applying programmed artificial neuronal networks (ANN) in Python language. The proposed supervised learning method allowed the classification of 6 ADLs through 64 signals from inertial sensors that made up the database (dataset) to train a multilayer perceptron (MLP), achieving an accuracy of 99%. Also, by applying dimensionality reduction to dataset, it was possible to reach 80% of accuracy in the classification. These results can potentially help the specialist to reduce costs and achieve a more accurate evaluation, seeking to improve monitoring and quality in the rehabilitation of these patients. |
| Author | SanPablo, A. I. P. Bastian, J. A. Montero, L. R. |
| Author_xml | – sequence: 1 givenname: L. R. surname: Montero fullname: Montero, L. R. email: lizethrodriguez@lasallistas.org.mx organization: Universidad La Salle,Mexico City,Mexico – sequence: 2 givenname: J. A. surname: Bastian fullname: Bastian, J. A. organization: Groups of Applied Intelligent Systems and Collaborative Assistance Robotics – sequence: 3 givenname: A. I. P. surname: SanPablo fullname: SanPablo, A. I. P. organization: Universidad La Salle,Mexico City,Mexico |
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| Snippet | Parkinson's disease (PD) is a progressive disease characterized by slow movements, tremors, and postural instability [1]. Inertial sensors provide valuable... |
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| SubjectTerms | Artificial neural networks Artificial Neuronal Networks Deep learning Inertial sensors Monitoring Parkinson's disease Sensor systems Supervised learning |
| Title | Classification of Activities of Daily Living in Subjects with Parkinson's Disease using Artificial Neural Networks |
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