Data Reconfiguration Algorithm for Efficient Learning State Classifications Based on Pupil Sizes 


Vol. 45,  No. 10, pp. 1756-1766, Oct.  2020
10.7840/kics.2020.45.10.1756


PDF
  Abstract

With the recent development of smart devices, the use of wearable devices became popular, which enabled us to collect individual biometric data such as heart rate, energy consumption and number of steps smoothly. As high performance and high accurate sensors are equipped in wearable devices, the more biometric data can be collected. Since the biometric data are less susceptible to racial and cultural differences, they can be used as a reliable set of data sources. Because of inevitable noises included in the processes of data acquisition, however, the performance of the machine learning algorithms employed for efficiently processing the biometric data and accurately analyzing them can be limited. In this paper, we focus on the sizes of pupil data among a variety of biometric data, which are used as an indicator of concentration. We adopt machine learning based classification algorithms for inferring the degree of concentration by analyzing the time-varying changes of pupil sizes. In order to improve the classification performance, we propose a pre-processing algorithm for data reconfiguration. Our simulation and experiment results confirm that the proposed pre-processing for data reconfiguration improves the data classification performance.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Lee, I. Jo, H. Park, "Data Reconfiguration Algorithm for Efficient Learning State Classifications Based on Pupil Sizes," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 10, pp. 1756-1766, 2020. DOI: 10.7840/kics.2020.45.10.1756.

[ACM Style]

Jungjin Lee, Il-Hyun Jo, and Hyunggon Park. 2020. Data Reconfiguration Algorithm for Efficient Learning State Classifications Based on Pupil Sizes. The Journal of Korean Institute of Communications and Information Sciences, 45, 10, (2020), 1756-1766. DOI: 10.7840/kics.2020.45.10.1756.

[KICS Style]

Jungjin Lee, Il-Hyun Jo, Hyunggon Park, "Data Reconfiguration Algorithm for Efficient Learning State Classifications Based on Pupil Sizes," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 10, pp. 1756-1766, 10. 2020. (https://doi.org/10.7840/kics.2020.45.10.1756)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880