PinPointer: An offline error measurement system for motor behavior research.

Abstract

The ability to precisely and accurately measure the distance between an implement and target point (i.e., the radial error) is crucial to conducting rigorous motor behavior research. However, manually measuring this error after each trial may be time-consuming and error-prone. In this paper, we present PinPointer, an error measurement system that enables motor behavior researchers to quickly and easily calculate x-axis, y-axis, and radial error distances. PinPointer automatically calculates descriptive statistical measures for each error type which can be exported for additional analysis. Our inter-rater and intra-rater reliability evaluations reveal that PinPointer has perfect absolute agreement and perfect consistency across eight raters and near-perfect to perfect intra-rater absolute agreement between two ratings of the same rater. PinPointer also demonstrated perfect fidelity to real-world measurements. PinPointer's Python-based source code is available as free and open source for other researchers to use and easily modify for their own applications and requirements. In addition, the PinPointer executable can be run on Windows or MacOS without installation or knowledge of Python programming. We hope PinPointer will prove to be a useful tool for motor behavior researchers to improve the speed, accuracy, and reliability of their error measurements.