Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Seo, Kyoungwon; Lee, Ahreum; Kim, Jieun; Ryu, Hokyoung; Choi, Hojin

doi:10.3791/57284

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…With this new data, we can classify patients using many new machine learning techniques (e.g. 16 , 38 , 59 , 60 ), in contrast to traditional assessments that use interviews and paper-and-pencil tests.…”

Section: Discussionmentioning

confidence: 99%

The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment

Ryu

Seo

2021

Sci Rep

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The illusion of having a large body makes us perceive objects as smaller than they really are. This action-specific perception effect occurs because we perceive the property of an object (i.e., size) differently according to our unique action capability (i.e., the affordance of body size). Although the body-ownership illusion contributing to this action-specific perception has been studied, its effects remain unclear in neurological patients. We examined the action-specific perception impairments of MCI patients by means of body-ownership illusion in a non-immersive virtual reality environment. Twenty healthy young adults, 21 healthy older adults, and 15 MCI patients were recruited. We assessed their “original-body action-specific perception” and “enlarged-body action-specific perception” using the original and enlarged sizes of their virtual bodies, respectively. The MCI patients’ original-body action-specific perception was no different than that of the healthy controls (p = 0.679). However, the enlarged-body action-specific perception of the MCI patients was significantly biased (p < 0.001). The inclusion of the enlarged-body action-specific perception provides additional discriminative power for early diagnosis of MCI (89.3% accuracy, 75.0% sensitivity, 100.0% specificity, and 87.5% balanced accuracy).

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Section: Discussionmentioning

confidence: 99%

The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment

Ryu

Seo

2021

Sci Rep

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“…Virtual reality (VR) technology allows behavioral data to be collected while performing IADL tasks in a controlled environment [33][34][35][36]. In addition, as VR provides a fully immersive experience of the real world in a noninvasive way, participants can naturally interact with their virtual environment, allowing ecologically valid real time data to be collected [25,37,38].…”

Section: Introductionmentioning

confidence: 99%

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

Kim,

Park,

Choi

et al. 2023

J Med Internet Res

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Background With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a “virtual kiosk test” for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment. Objective We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features. Methods A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test—developed by our group—where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls. Results In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=–3.44; P=.004), and a greater number of errors (t49=–3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score. Conclusions Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

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Visualization and Interaction Technologies in Serious and Exergames for Cognitive Assessment and Training: A Survey on Available Solutions and Their Validation

2022

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Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Cited by 7 publications

References 17 publications

The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment

The illusion of having a large virtual body biases action-specific perception in patients with mild cognitive impairment

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

Visualization and Interaction Technologies in Serious and Exergames for Cognitive Assessment and Training: A Survey on Available Solutions and Their Validation

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