Abstract
Although the number of patients with dementia has been increasing with the aging of society, no efficient treatment for advanced dementia forms has been proposed yet. Therefore, it is important to detect mild cognitive impairment (MCI) to prevent dementia from further progressing. According to a recent study, people with MCI tend to perform more inefficiently compared with healthy older adults during performance-based tests on instrumental activities of daily living (IADL). In this research, we aim to develop a discriminative model to identify motion primitives based on virtual reality-based IADL. In the experiment, finger movement was measured through the Lunchbox Task simulating meal preparation using a touch panel and a three-dimensional motion sensor. The time series velocity was estimated from the obtained position data, and segmentation was conducted based on the property of the reaching motion between two points. Considering the data of each segment interval, feature extraction and coding were performed according to the predefined motion primitives, and modeling based on machine learning was implemented. As a result, the identification accuracy of motion primitives was 97.1%. Sensitivity by category was 99% for stationary actions, 93% for pointing, 97% for drag, and 93% for click or release. These results indicate that hand movements during VR-IADL can be classified into four categories based on their behavioral characteristics. On this basis, the automatic identification of the motion primitive in VR-IADL is deemed realizable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Alzheimer Report 2018: The Global Impact of Dementia, p. 34 (2018)
Comprehensive Promotion of Measures for Dementia ‐ Ministry of Health, Labour and Welfare (2019). https://www.mhlw.go.jp/content/12300000/000519620.pdf
Yasuji, Y.: Current topics about mild cognitive impairment (MCI). Psychiatria et Neurologia Jponica 113(6), 584–592 (2011)
Malek-Ahmadi, M.: Reversion from mild cognitive impairment to normal cognition: a meta-analysis. Alzheimer Dis. Assoc. Disord. 30(4), 324–330 (2016)
Shojei, K., Yuta, S., Akiko, K., Toshiaki, K., Hidenori, I., Akira, H.: A preliminary study of speech peosody-based. Trans. Jpn. Soc. Artif. Intell. 26(2), 347–352 (2011)
Fujiwara, Y., et al.: Brief screening tool for mild cognitive impairment in older Japanese: validation of the Japanese version of the montreal cognitive assessment. Geriatr. Gerontol. Int. 10(3), 225–232 (2010)
Wadley, V.G., Okonkwo, O., Crowe, M., Ross-Meadows, L.A.: Mild cognitive impairment and everyday function: evidence of reduced speed in performing instrumental activities of daily living. Am. J. Geriatr. Psychiatry 16(5), 416–424 (2008)
Schmitter-Edgecombe, M., McAlister, C., Weakley, A.: Naturalistic assessment of everyday functioning in individuals with mild cognitive impairment: the day-out task. Neuropsychology 26(5), 631–641 (2012)
Schwartz, M.F., Segal, M.E., Veramonti, T., Ferraro, M.K., Buxbaum, L.J.: The naturalistic action test: a standardised assessment for everyday action impairment. Neuropsychological 12(4), 311–339 (2002)
Yamaguchi, T., Foloppe, D.A., Richard, P., Richard, E., Allain, P.: A dual-modal virtual reality kitchen for (re) learning of everyday cooking activities in Alzheimer’s disease. Presence Teleoperators Virtual Environ. 21(1), 43–57 (2012)
Giovannetti, T., et al.: The virtual kitchen challenge: preliminary data from a novel virtual reality test of mild difficulties in everyday functioning. Neuropsychological 26(6), 823–841 (2019)
Shirotori, A., et al.: Topic Model-based Clustering for IADL Motion Primitives (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Iwashita, Y., Yamaguchi, T., Giovannetti, T., Sakamoto, M., Ohwada, H. (2020). Discriminative Model for Identifying Motion Primitives Based on Virtual Reality-Based IADL. In: Stephanidis, C., Antona, M., Gao, Q., Zhou, J. (eds) HCI International 2020 – Late Breaking Papers: Universal Access and Inclusive Design. HCII 2020. Lecture Notes in Computer Science(), vol 12426. Springer, Cham. https://doi.org/10.1007/978-3-030-60149-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-60149-2_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60148-5
Online ISBN: 978-3-030-60149-2
eBook Packages: Computer ScienceComputer Science (R0)