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Estimating Working Stressor Based on Pulse Wave

Published: 24 February 2017 Publication History

Abstract

In this paper, we estimate workers' stressors from their pulse waves. When human beings are provided stressful stimulus from outside, their pulse wave would change. Since inexpensive commercial sensors of pulse waves are available nowadays, we can expect to detect stresses of workers in daily working environment. We assume four kinds of stressors. We extract ten kinds of features from the workers' pulse wave. We learn features using the random forest, which is a machine learning algorithm good at discrimination with many input variables. It is possible to create a model to identify four types of stress. We conducted an experiment to verify the effectiveness of the model. As a result of the experiment, the average of the F-measure was 0.32. From this result, it is suggested that the stressor could be identified from the pulse waves.

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  • (2023)A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the MilitaryBrain Sciences10.3390/brainsci1308115713:8(1157)Online publication date: 2-Aug-2023

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ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2017

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Author Tags

  1. Biological data
  2. Depression
  3. Machine learning
  4. Stressor

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  • (2023)A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the MilitaryBrain Sciences10.3390/brainsci1308115713:8(1157)Online publication date: 2-Aug-2023

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