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)
DOI: 10.4018/IJSSCI.2018010103

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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Problem Representation

In this study, we held the experiments of everyday activities tasks of preparing breakfast on a virtual reality-based software called Virtual Kitchen (VK) (Martono, et. al., 2016). This study has the goal of generating rules on the nature of subtle errors (hereby called micro errors) by collecting finger movement and analyzing the basic transition patterns of study participants doing daily activity tasks in a virtual reality-based environment, using a machine learning approach. Another purpose is to explore the use of analyzing finger speed when micro errors occur. In this study, the micro errors that will be used in the analysis are limited to two types: Reach - Touch (RT) errors and Reach - No Touch (RNT) errors. The other type of micro-errors: reach with object is not considered in the study, because in doing the cognitive everyday activities task in the VK both errors are not applicable due to virtual reality software limitations. In this study, we will be working with the combination of qualitative and quantitative data as input information for the machine learning process. The challenges are how to generate such input that can be used to extract useful output from the learning.

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