TY - GEN
T1 - Classification of Mental State from Sub-THz Reflections from the Skin
AU - Goldstein, Anna Kochnev
AU - Goldstein, Yoav
AU - Ishai, Paul Ben
AU - Feldman, Yuri
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - By applying machine learning techniques on sub-THz reflectance measurements from the palms of 21 subjects, this work presents preliminary evidence that the reflected electromagnetic (EM) signal is inherently different during stress and relaxation periods. Furthermore, the obtained results demonstrate that this difference can be determined from a 30second-long measurement with an accuracy of 92% for binary classification between relaxation and stress and an accuracy of 88% for multiclass classification between relaxation, mental stress, and physical stress.
AB - By applying machine learning techniques on sub-THz reflectance measurements from the palms of 21 subjects, this work presents preliminary evidence that the reflected electromagnetic (EM) signal is inherently different during stress and relaxation periods. Furthermore, the obtained results demonstrate that this difference can be determined from a 30second-long measurement with an accuracy of 92% for binary classification between relaxation and stress and an accuracy of 88% for multiclass classification between relaxation, mental stress, and physical stress.
UR - https://www.scopus.com/pages/publications/85139818083
U2 - 10.1109/IRMMW-THz50927.2022.9895482
DO - 10.1109/IRMMW-THz50927.2022.9895482
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AN - SCOPUS:85139818083
T3 - International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz
BT - IRMMW-THz 2022 - 47th International Conference on Infrared, Millimeter and Terahertz Waves
PB - IEEE Computer Society
T2 - 47th International Conference on Infrared, Millimeter and Terahertz Waves, IRMMW-THz 2022
Y2 - 28 August 2022 through 2 September 2022
ER -