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Clustering Finger Motion Data From Virtual Reality-Based Training to Analyze Patients With Mild Cognitive Impairment
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Author(s): Niken Prasasti Martono (Tokyo University of Science, Japan), Takehiko Yamaguchi (Tokyo University of Science, Japan), Takuya Maeta (Tokyo University of Science, Japan), Hibiki Fujino (Tokyo University of Science, Japan), Yuki Kubota (Tokyo University of Science, Japan), Hayato Ohwada (Tokyo University of Science, Japan)and Tania Giovanneti (Temple University, USA)
Copyright: 2018
Pages: 16
Source title:
Virtual and Augmented Reality: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-5469-1.ch064
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Abstract
Research in virtual reality (VR) has resulted in the development of many applications in clinical settings in the areas of learning and therapy in psychology and neuropsychology because this technology can be flexible to the needs of the clinical application. VR technology has many implementations for cognitive training and as a screening tool for patients with mild cognitive impairment (MCI). The technology has been used in the screening, diagnosis, treatment and support of patients with MCI. This study found that the information recorded in VR-based learning software can be useful in analyzing individuals with MCI in order to characterize groups of participants. The authors implemented a time series clustering algorithm acting on finger motion data from nine healthy participants as a pilot study, then comprehensively reviewed the clustering result by comparing it with performance-based measures. The results indicate that the clusters formed by using the acceleration data is reasonably analogous to the performance measures (i.e., with respect to the type and number of errors that occurred).
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