Validation of a Velocity-based Algorithm to Quantify Saccades During Walking and Turning in Mild Traumatic Brain Injury and Healthy Controls

Objective: Saccadic (fast) eye movements are a routine aspect of neurological examination and are a potential biomarker of mild traumatic brain injury (mTBI). Objective measurement of saccades has become a prominent focus of mTBI research, as eye movements may be a useful assessment tool for deficits in neural structures or processes. However, saccadic measurement within mobile infra-red (IR) eye-tracker raw data requires a valid algorithm. The objective of this study was to validate a velocity-based algorithm for saccade detection in IR eye-tracking raw data during walking (straight ahead and while turning) in people with mTBI and healthy controls.

Approach: Eye-tracking via a mobile IR Tobii Pro Glasses 2 eye-tracker (100 Hz) was performed in people with mTBI (n  =  10) and healthy controls (n  =  10). Participants completed two walking tasks: straight walking (walking back and forth for 1 min over a 10 m distance), and walking and turning (turns course included 45°, 90° and 135° turns). Five trials per subject, for one-hundred total trials, were completed. A previously reported velocity-based saccade detection algorithm was adapted and validated by assessing agreement between algorithm saccade detections and the number of correct saccade detections determined from manual video inspection (ground truth reference).

Main Results: Compared with video inspection, the IR algorithm detected ~97% (n  =  4888) and ~95% (n  =  3699) of saccades made by people with mTBI and controls, respectively, with excellent agreement to the ground truth (intra-class correlation coefficient  =  .979 to .999).

Significance: This study provides a simple yet highly robust algorithm for the processing of mobile eye-tracker raw data in mTBI and controls. Future studies may consider validating this algorithm with other IR eye-trackers and populations.