Research on Updating of Body Schema Using AR Limb and Measurement of the Updated Value

Nakamura, Sousuke; Mochizuki, Naoki; Konno, Tomoki; Yoda, Junya; Hashimoto, Hideki

doi:10.1109/jsyst.2014.2373394

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…In both driving a car and remote operation of a robot, the machine (car or robot) must be manipulated like one’s own body. This sensation of body ownership is a type of BS modification [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts

Suzuki

2017

Brain Inf.

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This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7–BA40–BA21 in the right hemisphere became significantly activated (, , and , respectively) during BS modification while performing the hand-tracing task.

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Section: Introductionmentioning

confidence: 99%

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts

Suzuki

2017

Brain Inf.

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“…al., 2008)(Sanchez-Vives, et al, 2010) (Yuan and Steed, 2010) ( Tajadura (Jordan and Rumelhart, 1992) ) ( ) 2 (Kawato, et al, 1987) (Kawato and Gomi, 1992) (Oyama, et al, 1993) (Sato, 1992) (Stark, et al, 1987) da Vinci (Guthart and Salisbury , 2000) ( (Furukawa, et al, 2014) (Tachi, et al, 1988(Tachi, et al, )( , 1994 VR VR ( , 2005)( , 2009) ) (Fuentes, et al, 2013) (Thompson and Spana, 1988) (Longo and Haggard, 2005) 3. AR (Maravita and Iriki, 2004) (Roschin, et al, 2011) (Fuke, et al, 2009) ( ) (Nakamura, et al, 2014…”

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confidence: 99%

“…4. (Oyama, et al, 2002) (Prablanc, et al, 1979) (Nakamura, et al, 2014) 4·1 HMD VR ( ) ( 6) HMD ( ) Fig. 7 Conceputual image of ideal point calculation after a tracing task Fig.…”

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confidence: 99%

Effect of the update of position sense using AR-limb on the motion trajectory

Yoda

Nakamura

Konno

et al. 2015

Transactions of the JSME (In Japanese)

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The position sense of a human body is a basis for performing various body motions, especially high-speed. Therefore, the mismatch of the position sense generates the lack of performance , for example , in case of immersive interfaces so called Telepresence and sports motion . In response the authors have been researching on the method using AR-Limb which aims to adjust or control the human position sense. However, the current research only discussed the update through static measurement and the measurement through dynamic motions was out of scope. Therefore, this paper introduced the method to measure the state of position sense during the actual motion, and evaluated the effect of update of position sense on the body motion. As an experimental result, it was verified that the body motion was completely effected by the update of position sense.

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Hand-guide Training Based on Integration of Force Information Obtained by Sensor and State Observer

Nagatsu

Hashimoto

2021

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)

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Research on Updating of Body Schema Using AR Limb and Measurement of the Updated Value

Cited by 8 publications

References 35 publications

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts

Effect of the update of position sense using AR-limb on the motion trajectory

Hand-guide Training Based on Integration of Force Information Obtained by Sensor and State Observer

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