Journal of Textile Engineering
Online ISSN : 1880-1986
Print ISSN : 1346-8235
ISSN-L : 1346-8235
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Original Papers
  • Tomoe MASUDA
    Type: research-article
    2020 Volume 66 Issue 6 Pages 93-107
    Published: December 15, 2020
    Released: March 02, 2021
    JOURNALS FREE ACCESS

    From the perspective of 3D custom-made garment design, the 3D-body curved surface shapes of 1,144 males in an extensive age group (18 to 86 years old) were investigated using the angle values of three curvatures (Kc, kc, and Hc) by multivariate analysis. From the 3D data, fourteen 3D-body shape types were categorized using the sum angle values of each of the elliptical (+Kc), the hyperbolic (-Kc), the convex (+Hc), and the concave (-Hc) curved surface shapes of ten areas. There was one body type in the 20s group, three in each of the 30s, younger 40s, elder 40s groups, 50s groups, and one in the 60s group. The curvature values and the curvature color map of the 3D-body surfaces clearly displayed the difference in height between the convex elliptic curved surfaces in the abdomen, buttocks, and legs areas due to the change with age.

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  • Fangmeng ZENG, Yitao LIN, Panote SIRIARAYA, Dongeun CHOI, Noriaki KUW ...
    Type: research-article
    2020 Volume 66 Issue 6 Pages 109-117
    Published: December 15, 2020
    Released: March 02, 2021
    JOURNALS FREE ACCESS

    The global population is ageing; exacerbating a range of age-related health problems, like dementia. In the late stage of dementia, patients often are unable to find words to express their feelings; causing serious challenges in healthcare. Our aim is to detect the emotions of elderly patients using physiological signals - electroencephalogram (EEG) and electrocardiogram (ECG) - using deep learning neural networks. However, most EEG and ECG monitoring devices are uncomfortable and not suitable for daily wear by elderly people. For this study, a prior experiment was conducted on 5 healthy elderly subjects for binary classification of positive and negative emotions: EEG and ECG data were collected from the subjects, using our own designed wearable textile devices while they watch selected stimuli. We propose an end-to-end deep learning method - Long short-term memory (LSTM) - to detect emotion from raw clean signals after removing noises and baseline wander. LSTM can learn features from raw data directly and achieve binary emotion classification with an accuracy of 76.67% with EEG signals, 75.00% with ECG signals, and 95.00% with EEG and ECG signals, respectively. This proposed system for detecting emotion by deep learning method using our userfriendly and easy-to-wear textile devices offer great prospects for use in everyday care situations and dementia care.

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