Machine Learning Techniques for Parkinson’s Disease Detection using Wearables during a Timed-up-and-Go-Test
In this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding mod...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
De Gruyter
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ee3ed0d612dc4f4894c6e4ec4ab07546 |
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| Sumario: | In this paper, the classification models for Idiopathic Parkinson's syndrome (iPS) detection through timed-up-and-go test performed on iPS-patients are given. The models are based on the supervised learning. The data are extracted via Myo gesture armband worn on two hands. The corresponding models are based on extracted features from signal data and raw signal data respectively. The achieved accuracy from both models are 0.91 and 0.93 with reasonable specificity and sensitivity. |
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