Feature Extraction based on Touch Interaction Data in Virtual Reality-based IADL for Characterization of Mild Cognitive Impairment

Yuki Kubota; Takehiko Yamaguchi; Takuya Maeta; Yosuke Okada; Yoshihito Miura; Niken Prasasti Martono; Hayato Ohwada; Giovannetti Tania

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Link to original content: https://doi.org/10.5220/0006265201520157

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Paper

Feature Extraction based on Touch Interaction Data in Virtual Reality-based IADL for Characterization of Mild Cognitive Impairment

Topics: Analysis Methods and Tools; Task Analysis, Guidelines and Heuristics; User Performance Evaluation; Virtual, Augmented and Mixed Reality

In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISIGRAPP, 152-157, 2017 , Porto, Portugal

Authors: Yuki Kubota ¹ ; Takehiko Yamaguchi ² ; Takuya Maeta ¹ ; Yosuke Okada ² ; Yoshihito Miura ² ; Niken Prasasti Martono ² ; Hayato Ohwada ² and Giovannetti Tania ³

Affiliations: ¹ Graduated of Tokyo University of Science, Japan ; ² Tokyo University of Science, Japan ; ³ Temple University, United States

Keyword(s): Dementia, MCI, Screening, VR-IADK, Characterization.

Abstract: The aim of this study was to explore the feature pattern of Mild Cognitive Impairment (MCI) in Virtual Reality based Instrumental Activities of Daily Living (VR-IADL) which runs on a tablet PC as well as requires participants a touch interaction to complete the task. Twelve participants (MCI: 4, history of MCI: 2, healthy elderly: 6) were recruited from the region of Philadelphia in USA to perform a VR-IADL task. We found that Non Touch Time (NTT) which is time interval during not touching screen on tablet was longer than that of MCI patients as well as healthy older adults with having history of MCI. Several types of feature patterns were extracted from the NTT such as … Based on the feature pattern, Support Vector Machine (SVM) was performed to calculate the accuracy of the feature patter for characterization of MCI. As the result, the identification rate was 75%.

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Paper citation in several formats:

Kubota, Y.; Yamaguchi, T.; Maeta, T.; Okada, Y.; Miura, Y.; Martono, N.; Ohwada, H. and Tania, G. (2017). Feature Extraction based on Touch Interaction Data in Virtual Reality-based IADL for Characterization of Mild Cognitive Impairment. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - HUCAPP; ISBN 978-989-758-229-5; ISSN 2184-4321, SciTePress, pages 152-157. DOI: 10.5220/0006265201520157

@conference{hucapp17,
author={Yuki Kubota. and Takehiko Yamaguchi. and Takuya Maeta. and Yosuke Okada. and Yoshihito Miura. and Niken Prasasti Martono. and Hayato Ohwada. and Giovannetti Tania.},
title={Feature Extraction based on Touch Interaction Data in Virtual Reality-based IADL for Characterization of Mild Cognitive Impairment},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - HUCAPP},
year={2017},
pages={152-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006265201520157},
isbn={978-989-758-229-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - HUCAPP
TI - Feature Extraction based on Touch Interaction Data in Virtual Reality-based IADL for Characterization of Mild Cognitive Impairment
SN - 978-989-758-229-5
IS - 2184-4321
AU - Kubota, Y.
AU - Yamaguchi, T.
AU - Maeta, T.
AU - Okada, Y.
AU - Miura, Y.
AU - Martono, N.
AU - Ohwada, H.
AU - Tania, G.
PY - 2017
SP - 152
EP - 157
DO - 10.5220/0006265201520157
PB - SciTePress