TY - GEN
T1 - Application of Deep Learning for Ergonomic Data Augmentation and Human State Recognition
AU - Banchi, Yoshihiro
AU - Kawai, Takashi
AU - Tomino, Nagakazu
AU - Yamagata, Tomohiro
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In ergonomic experiments, a small number of participants is often a problem because a sufficient amount of data is not obtained. In recent years, human state recognition is wide-spread, and estimating the human state from biological information acquired from a wearable device, is useful for improving living behavior. While it is necessary to collect a sufficient amount of data in order to perform state estimation with a certain degree of accuracy, collecting the amount of data requires a considerable cost. This study attempted to expand physiological and psychological data using deep learning. Specifically, information on physiological indicators was added to ACGAN. From the verification using the actual experimental results, it was found that the accuracy of recognizing the human state was improved by using the augmented data compared to the case of learning with a small number of original data.
AB - In ergonomic experiments, a small number of participants is often a problem because a sufficient amount of data is not obtained. In recent years, human state recognition is wide-spread, and estimating the human state from biological information acquired from a wearable device, is useful for improving living behavior. While it is necessary to collect a sufficient amount of data in order to perform state estimation with a certain degree of accuracy, collecting the amount of data requires a considerable cost. This study attempted to expand physiological and psychological data using deep learning. Specifically, information on physiological indicators was added to ACGAN. From the verification using the actual experimental results, it was found that the accuracy of recognizing the human state was improved by using the augmented data compared to the case of learning with a small number of original data.
KW - Data augmentation
KW - Deep learning
KW - Human state recognition
UR - http://www.scopus.com/inward/record.url?scp=85111158955&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-74611-7_68
DO - 10.1007/978-3-030-74611-7_68
M3 - Conference contribution
AN - SCOPUS:85111158955
SN - 9783030746100
T3 - Lecture Notes in Networks and Systems
SP - 504
EP - 507
BT - Proceedings of the 21st Congress of the International Ergonomics Association, IEA 2021 - Healthcare and Healthy Work
A2 - Black, Nancy L.
A2 - Neumann, W. Patrick
A2 - Noy, Ian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st Congress of the International Ergonomics Association, IEA 2021
Y2 - 13 June 2021 through 18 June 2021
ER -