Our paper related to human activity recognition was presented in The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)
Kenta Hayashi, Shingo Kumazoe, Shigemi Ishida, Yutaka Arakawa
Distinguishing Working State by Palm Orientation Proceedings Article
In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021), 2021.
@inproceedings{Hayashi2021,
title = {Distinguishing Working State by Palm Orientation},
author = {Kenta Hayashi, Shingo Kumazoe, Shigemi Ishida, Yutaka Arakawa},
url = {https://ieeexplore.ieee.org/document/9391950},
doi = {10.1109/LifeTech52111.2021.9391950},
year = {2021},
date = {2021-03-10},
urldate = {2021-03-10},
booktitle = {2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)},
abstract = {When working from home, self-management becomes of paramount importance due to the absence of a boss or colleagues. As a result, individuals tend to waste time surfing the internet and playing with our smartphones. We propose a wrist-worn sensor-based system that identifies whether a desk worker is working or not for self-management and productivity. Our main hypothesis is that the identification of the various tasks that occur during desk work, such as using computers, reading books, manipulating a smartphone, and writing, can be simply distinguished by the direction of the palm. In this paper, to verify our hypothesis, we measure various tasks with the wrist-worn sensor attached to clarify the relationship between hand orientation and each task. At the same time, we develop a machine learning-based classifier to distinguish between the states of `working' and `not-working' using the obtained hand orientation data. We performed 10-fold cross-validation and Leave-One-Person-Out validation and we found that it was possible to distinguish whether a desk worker is working or not with an F1-value of 0.8 or higher.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When working from home, self-management becomes of paramount importance due to the absence of a boss or colleagues. As a result, individuals tend to waste time surfing the internet and playing with our smartphones. We propose a wrist-worn sensor-based system that identifies whether a desk worker is working or not for self-management and productivity. Our main hypothesis is that the identification of the various tasks that occur during desk work, such as using computers, reading books, manipulating a smartphone, and writing, can be simply distinguished by the direction of the palm. In this paper, to verify our hypothesis, we measure various tasks with the wrist-worn sensor attached to clarify the relationship between hand orientation and each task. At the same time, we develop a machine learning-based classifier to distinguish between the states of `working' and `not-working' using the obtained hand orientation data. We performed 10-fold cross-validation and Leave-One-Person-Out validation and we found that it was possible to distinguish whether a desk worker is working or not with an F1-value of 0.8 or higher.