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An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment

An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment

Niken Prasasti Martono (Tokyo University of Science, Noda, Japan), Keisuke Abe (Tokyo University of Science, Noda, Japan), Takehiko Yamaguchi (Tokyo University of Science, Suwa, Japan), and Hayato Ohwada (Department of Industrial Administration, Tokyo University of Science, Noda, Japan)
Copyright: © 2018 |Volume: 10 |Issue: 1 |Pages: 11
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781522544029|DOI: 10.4018/IJSSCI.2018010103
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MLA

Martono, Niken Prasasti, et al. "An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment." IJSSCI vol.10, no.1 2018: pp.27-37. https://doi.org/10.4018/IJSSCI.2018010103

APA

Martono, N. P., Abe, K., Yamaguchi, T., & Ohwada, H. (2018). An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment. International Journal of Software Science and Computational Intelligence (IJSSCI), 10(1), 27-37. https://doi.org/10.4018/IJSSCI.2018010103

Chicago

Martono, Niken Prasasti, et al. "An Analysis of Motion Transition in Subtle Errors using Inductive Logic Programming: A Case Study in Approaches to Mild Cognitive Impairment," International Journal of Software Science and Computational Intelligence (IJSSCI) 10, no.1: 27-37. https://doi.org/10.4018/IJSSCI.2018010103

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Abstract

This article seeks to utilize the data collected from virtual reality (VR)-based software and a leap-motion device used for learning of subtle errors in mild cognitive impairment (MCI) cases to enable early detection of MCI by analyzing the classification rules for errors (action slips) based on finger-action transitions when performing instrumental activities of daily living (IADL). Finger motion was recorded as a time-series database. An induction technique known as Inductive-Logic Programming (ILP), which uses logical and clausal language to represent the training data, was then used to discover a concise classification rule using logical programming. The content within this article was able to generate rules on how action transitions of the finger in the experiments were related to the pattern of micro-errors that indicate the difference of error regarding the length of the no-motion state of the finger.

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