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Meas."],"published-print":{"date-parts":[[2019,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : 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.\n                    <jats:italic>Approach<\/jats:italic>\n                    : Eye-tracking via a mobile IR Tobii Pro Glasses 2 eye-tracker (100\u2009Hz) was performed in people with mTBI (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009\u2009=\u2009\u200910) and healthy controls (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009\u2009=\u2009\u200910). Participants completed two walking tasks: straight walking (walking back and forth for 1\u2009min over a 10 m distance), and walking and turning (turns course included 45\u00b0, 90\u00b0 and 135\u00b0 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).\n                    <jats:italic>Main results<\/jats:italic>\n                    : Compared with video inspection, the IR algorithm detected ~97% (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009\u2009=\u2009\u20094888) and ~95% (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009\u2009=\u2009\u20093699) of saccades made by people with mTBI and controls, respectively, with excellent agreement to the ground truth (intra-class correlation coefficient\n                    <jats:sub>2,1<\/jats:sub>\n                    \u2009\u2009=\u2009\u2009.979 to .999).\n                    <jats:italic>Significance<\/jats:italic>\n                    : 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.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ab159d","type":"journal-article","created":{"date-parts":[[2019,4,3]],"date-time":"2019-04-03T18:18:12Z","timestamp":1554315492000},"page":"044006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Validation of a velocity-based algorithm to quantify saccades during walking and turning in mild traumatic brain injury and healthy controls"],"prefix":"10.1088","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-9372","authenticated-orcid":false,"given":"Samuel","family":"Stuart","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucy","family":"Parrington","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas","family":"Martini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryana","family":"Popa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter C","family":"Fino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurie A","family":"King","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"pmeaab159dbib001","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596","type":"book","author":"American Psychiatric Association","year":"2013","edition":"5th edn"},{"key":"pmeaab159dbib002","doi-asserted-by":"publisher","first-page":"616","DOI":"10.3758\/s13428-016-0738-9","type":"journal-article","article-title":"One algorithm to rule them all? 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