Conference Proceedings

Servo-Gaussian Model to Predict Success Rates in Manual Tracking: Path Steering and Pursuit of 1D Moving Target

Abstract

We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D mov-ing target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints forma bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls in-side the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R^2 > 0.92 and MAE < 4.9% for steering and R^2 > 0.95 and MAE < 6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

Information

Book title

Proceedings of the 33rd annual ACM symposium on User interface software and technology

Pages

844–857

Date of issue

2020/10/20

DOI

10.1145/3379337.3415896

Citation

Shota Yamanaka, Hiroki Usuba, Haruki Takahashi, Homei Miyashita. Servo-Gaussian Model to Predict Success Rates in Manual Tracking: Path Steering and Pursuit of 1D Moving Target, Proceedings of the 33rd annual ACM symposium on User interface software and technology, pp.844–857, 2020.

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