TY - JOUR

T1 - Stimuli-magnitude-adaptive sample selection for data-driven haptic modeling

AU - Abdulali, Arsen

AU - Hassan, Waseem

AU - Jeon, Seokhee

N1 - Funding Information: This research was supported by the Global Frontier Program (NRF-2012M3A6A3056074) and the ERC program (2011-0030075) both through NRF Korea, and by the ITRC program (IITP-2016-H8501-16-1015) through IITP Korea. Publisher Copyright: © 2016 by the authors; licensee MDPI, Basel, Switzerland.

PY - 2016/6

Y1 - 2016/6

N2 - Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output- and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior–force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

AB - Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output- and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior–force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

KW - Data-driven modeling

KW - Haptic feedback

KW - Regression

KW - Sample selection

UR - http://www.scopus.com/inward/record.url?scp=85028513956&partnerID=8YFLogxK

U2 - 10.3390/e18060222

DO - 10.3390/e18060222

M3 - Journal article

AN - SCOPUS:85028513956

VL - 18

JO - Entropy

JF - Entropy

SN - 1099-4300

IS - 6

M1 - 222

ER -