Research with wearable sensor
Principal Investigator(s): View help for Principal Investigator(s) Yoshiko Arima, Kyoto University of Advanced Science
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| data | 01/17/2025 02:15:AM |
Project Citation:
Arima, Yoshiko. Research with wearable sensor. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-03-23. https://doi.org/10.3886/E178601V1
Project Description
Summary:
View help for Summary
This
study measured
physical activity during remote work and explored the movements
associated with performance. During remote working, we almost sit in front of a
computer. Because the movements are slight, a new neural net should be created
to distinguish subtle chest movements. Study 1 classified chest movements into
six categories: walking, rising and sitting, standing still, rotating, swaying,
and rocking. (n = 8, ages 19–62, 6 males, 2 females) The LSTM model showed a
better correct response rate (M = 83.8%) than the CNN model. Study 2 showed
that chest swing
affects reaction
time and that reaction time affects work accuracy.
Funding Sources:
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MEXT/JSPS KAKENHI (JP21K02988)
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