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contributor authorGaang Lee
contributor authorSangHyun Lee
date accessioned2024-04-27T22:43:25Z
date available2024-04-27T22:43:25Z
date issued2024/05/01
identifier other10.1061-JCCEE5.CPENG-5665.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297340
description abstractWearable biosensors, in conjunction with machine learning, have been employed to develop less invasive monitoring techniques for assessing stress among construction workers during fieldwork. However, existing techniques face limitations in terms of scalable field application due to their subject and context dependency; it is difficult to apply them to new people in new contexts without additional labeled data collection. Therefore, this study developed a stress detection technique that incorporates domain adaptation, simultaneously learning a classifier and a subject- and context-independent features, in this way advancing generalizability. The proposed technique consistently demonstrated superior accuracy compared with benchmarks in classifying stress levels within a testing data set whose subjects and contexts were different from those of training data sets. Thus, the technique can advance generalizability across subjects and contexts. This finding can help us to reliably detect stress for new people in new contexts without additional labeled data collection, thereby contributing to scalable field application of wearable-based stress monitoring at construction sites.
publisherASCE
titleDeep-Learning Domain Adaptation to Improve Generalizability across Subjects and Contexts in Detecting Construction Workers’ Stress from Biosignals
typeJournal Article
journal volume38
journal issue3
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-5665
journal fristpage04024010-1
journal lastpage04024010-12
page12
treeJournal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 003
contenttypeFulltext


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