Fatigue Recognition Based on Audiovisual Content

Malov, Dmitrii; Shumskaya, Olga

doi:10.1109/summa48161.2019.8947571

Cited by 3 publications

(1 citation statement)

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“…In the MFCC image, the signal characteristics were expressed in the form of a pattern or texture. Therefore, deep learning models suitable for image pattern or texture type classification were selected [ 67 , 68 , 69 ]. The performance of the five CNN algorithms was evaluated using the five-fold cross-validation and hold-out methods, wherein the datasets were divided in a ratio of 8:1:1 for training, validation, and testing, respectively [ 70 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly

Ahn,

Cho,

Kim

et al. 2023

Bioengineering

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Background: Alzheimer’s disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment. Objective: Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics. Materials and Methods: The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results. Results: Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group. Conclusions: The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

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