DESIGN OF VEST PROTECTOR FOR SACRED PALANQUIN BEARER TO REDUCE FATIGUE LEVEL AND MEASUREMENT VIA BRAINWAVES
DOI:
https://doi.org/10.23055/ijietap.2020.27.6.6351Keywords:
Fatigue level measurement, sacred palanquin, brainwaves, vest protector designAbstract
Mazu’s circumnavigation is a major celebration event in Taiwan. The palanquin is a means of transportation for the Gods, and it is also a symbol of a sacred object. Believers participate in the practice of passing under the palanquin or carrying a palanquin as a way to approach the divine space where the gods are located. This study took the palanquin as the theme and the people who carry a palanquin as the research subjects. In common, the fatigue level measurement is measured via questionnaire. Measurement using questionnaire survey is very subjective to the user and also hard to be correctly measured. We proposed an alternative using quantitative measurement via brainwaves. The fatigue level of palanquin bearer will be calculated by the system. In this paper, we compare the fatigue level among palanquin bearers. The first group is palanquin bearer without using our vest protector, and the second group is palanquin bearer using our vest protector. Based on the spirit of user-centered design, the design of the palanquin protector can protect the safety of the palanquin bearers, relieve pain and enhance comfort, so that the palanquin bearers can continue to maintain the manpower palanquin bearing tradition and express their respect for their gods. We also propose a deep learning model to do the classification of the fatigue level of the palanquin bearer. Our deep learning method can predict until 77% of the accuracy which is the best among state-of-the art models. From the experiments we proved that our vest protector design can reduce the fatigue level of the palanquin bearers. The vest protector help 3 persons of palanquin bearer reduce their fatigue level from moderate fatigue to normal, and help 4 persons of palanquin bearer from severe fatigue into moderate fatigue. We also compare our deep learning model with other models and get the best results from the experiments.
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