Transfer Learning and Pressure Effect for Handwriting to Early Detection of Parkinson’s Disease

El Ghzizal, Yassir; Aharrane, Nabil; Khaissidi, Ghizlane; Mrabti, Mostafa

doi:10.1007/978-3-031-01942-5_46

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The method effectively extracted features, then output features were connected and fed to a fully connected layer for classification. Ghzizal et al [40] used transfer learning of a pre-trained CNN to classify patients with Parkinson's disease. Tactile perception is an important research direction in the field of robotics and artificial skin.…”

Section: A Velostat-based Applicationmentioning

confidence: 99%

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Yuan

2021

2021 IEEE Sensors

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With the emphasis on healthcare, early childhood education, and fitness, non-invasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied due to its advantages of simple structure, easy access, visualization application, and harmlessness. This paper introduces a smart pressure e-mat (SPeM) system based on a piezoresistive material Velostat for human monitoring applications, including sleeping postures, sports, and yoga recognition. After a subsystem scans e-mat readings and processes the signal, it generates a pressure image stream. Deep neural networks (DNNs) are used to fit and train the pressure image stream and recognize the corresponding human behavior. Four sleeping postures and five dynamic activities inspired by Nintendo Switch Ring Fit Adventure (RFA) are used as a preliminary validation of the proposed SPeM system. The SPeM system achieves high accuracies on both applications, which demonstrates the high accuracy and generalization ability of the models. Compared with other pressure sensor-based systems, SPeM possesses more flexible applications and commercial application prospects, with reliable, robust, and repeatable properties.

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Section: A Velostat-based Applicationmentioning

confidence: 99%

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Yuan

2021

2021 IEEE Sensors

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Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition

Yuan,

Wei,

2024

IEEE J. Sel. Areas Sensors

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Transfer Learning and Pressure Effect for Handwriting to Early Detection of Parkinson’s Disease

Cited by 2 publications

References 11 publications

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Smart Cushion Based on Pressure Sensor Array for Human Sitting Posture Recognition

Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition

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