A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster

Sang Muk Jo, Sung-Bae Cho

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Soft keyboard has been popular since the smartphone made an appearance. As the smartphone is a personal device used for user's preference, we need to personalize the soft keyboard. In order to make a personal context-aware keyboard, a large amount of data is required from each individual, but it is difficult to collect the data enough. In this paper, we propose a novel method to construct random forests with additional data of the same clusters, which copes with small amount of the individual data by adding similar data through K-means clustering algorithm. In addition, for the personalization of the soft keyboard, the preferred GUIs are recommended according to the activities and input hand postures of the user recognized by the random forest models trained with the clustered data. To train the proposed system, we have collected the data from 200 people. Each person can use the most necessary keyboard by selecting the appropriate GUI for each situation depending on the smartphone usage activity and input hand postures. The proposed system showed better performance than a model using all common data and a model using individual data only.

Original languageEnglish
Pages (from-to)17-27
Number of pages11
JournalNeurocomputing
Volume353
DOIs
Publication statusPublished - 2019 Aug 11

Fingerprint

Smartphones
Graphical user interfaces
Posture
Hand
Clustering algorithms
Cluster Analysis
Equipment and Supplies
Smartphone
Forests

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

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A personalized context-aware soft keyboard adapted by random forest trained with additional data of same cluster. / Jo, Sang Muk; Cho, Sung-Bae.

In: Neurocomputing, Vol. 353, 11.08.2019, p. 17-27.

Research output: Contribution to journalArticle

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