An assessment of the utility and functionality of wearable head impact sensors in Australian Football

McIntosh, Andrew S.; Willmott, Catherine; Patton, Declan A.; Mitra, Biswadev; Brennan, John P.; Dimech-Betancourt, Bleydy; Howard, Teresa; Rosenfeld, Jeffrey V.

doi:10.1016/j.jsams.2019.02.004

Cited by 24 publications

(31 citation statements)

References 20 publications

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“…If the non-game data is not removed, it would arti cially in ate the number of head impacts a player sustains. There were also a number of clear head impacts seen on video review that did not register on the sensor, consistent with previous x-patch™ studies [30,31]. There were a total of 2,399 video identi ed impacts to the head or body.…”

Section: Discussionsupporting

confidence: 78%

“…The ndings from this study are consistent with previous research, highlighting the importance of using a secondary source, such as video review, to verify and characterize x-patch™ recorded head impacts. That is, the x-patch™ has serious limitations as a primary data source [16,17,25,30]. The current study identi ed similar high rates of false-positive direct head impacts recorded by the wearable sensors in junior representative rugby league as were previously described in semi-professional men's rugby league and collegiate lacrosse respectively.…”

Section: Resultssupporting

confidence: 73%

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Video Analysis and Verification of Direct Head Impacts Recorded by Wearable Sensors in Junior Rugby League Players

Carey

Terry

McIntosh

et al. 2020

Preprint

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Background: Rugby League is a high-intensity collision sport that carries a risk of concussion. Youth athletes are considered to be more vulnerable and take longer to recover from concussion than adult athletes. Purpose: To review head impact events in elite level junior representative rugby league and to verify and analyze x-patchTM recorded impacts via video analysis.Study Design: Observational case series.Methods: The x-patchTM was used on twenty-one adolescent players (thirteen forwards and eight backs) during a 2017 junior representative rugby league competition. Game day footage, recorded by a trained videographer from a single camera, was synchronized with accelerometer timestamps. Impacts were double verified by video review. Impact rates, playing characteristics, and game play situations were described.Results: The x-patchTM recorded 624 impacts 20g between game start and finish, of which 564 (90.4%) were verified on video. Upon video review, 413 (73.2%) of all verified impacts 20g where determined to be direct head impacts. Direct head impacts 20g occurred at a rate of 5.2 impacts per game hour; 7.6 for forwards and 3.0 for backs (range=0-18.2). A defender’s arm directly impacting the head of the ball carrier was the most common event, accounting for 21.3% (n=120) of all impacts, and 46.7% of all “hit-up” impacts. There were no medically diagnosed concussions during the competition.Conclusion: The majority (90.4%) of impacts 20g recorded by the x-patchTM sensor were verified by video. Double verification of direct head impacts in addition to cross-verification of sensor recorded impacts using a secondary source such as synchronized video review can be used to ensure accuracy and validation of data.

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Section: Discussionsupporting

confidence: 78%

Section: Resultssupporting

confidence: 73%

Video Analysis and Verification of Direct Head Impacts Recorded by Wearable Sensors in Junior Rugby League Players

Carey

Terry

McIntosh

et al. 2020

Preprint

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“…Nearly two-thirds of the identified studies did not perform and/or report observer and/or video confirmation for all sensor-recorded events, which is a critical gap considering previous studies have found the absence of confirmation methodology can result in substantial overestimation of head impact exposure. 7,18,23,28,32,33,36 For example, Cortes et al 7 investigated male and female high school lacrosse players using two different sensors: male players wore the helmet-mounted GForceTracker (GForceTracker, Richmond Hill, ON), while female players wore the skin-affixed xPatch (X2 Biosystems, Seattle, WA). Of the sensor events recorded by the GForceTracker, 35% were false positives, whereas the xPatch demonstrated nearly twice as many false positives (68%).…”

Section: Confirmation Methodsmentioning

confidence: 99%

“…In a similar study of female collegiate soccer games, Press et al 33 found that the proprietary xPatch filtering algorithm incorrectly classified 43% of non-events, but only 4% of videoconfirmed impact events were incorrectly classified. McIntosh et al 23 reported the xPatch algorithm incorrectly classified 38% and 28% of video-confirmed impact events and non-events, respectively, in adult amateur Australian football. Patton et al 32 evaluated unfiltered data from the SIM-G in male and female high school soccer games using video review and reported that 30% of the algorithm-determined impact events were incorrectly classified, whereas 44% of the algorithm-determined non-events were incorrectly classified.…”

Section: Filtering Algorithmsmentioning

confidence: 99%

“…31 A wide variety of head impact sensors is currently available, such as instrumented helmets (e.g., GForceTracker), 5,7,26 headbands (e.g., SIM-G), 18,32,43 mouthguards (e.g., MIG) 16,46 and skin patches (e.g., xPatch). 23,28,33 Increasingly, research studies have used such technology to collect head impact data in sports, commonly American football. 30 Validation of kinematics recorded by head impact sensors in laboratory settings has typically used anthropometric test devices, 44 cadavers 17 and human volunteers.…”

Section: Introductionmentioning

confidence: 99%

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Head Impact Sensor Studies In Sports: A Systematic Review Of Exposure Confirmation Methods

et al. 2020

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To further the understanding of long-term sequelae as a result of repetitive head impacts in sports, in vivo head impact exposure data are critical to expand on existing evidence from animal model and laboratory studies. Recent technological advances have enabled the development of head impact sensors to estimate the head impact exposure of human subjects in vivo. Previous research has identified the limitations of filtering algorithms to process sensor data. In addition, observer and/or video confirmation of sensor-recorded events is crucial to remove false positives. The purpose of the current study was to conduct a systematic review to determine the proportion of published head impact sensor data studies that used filtering algorithms, observer confirmation and/or video confirmation of sensor-recorded events to remove false positives. Articles were eligible for inclusion if collection of head impact sensor data during live sport was reported in the methods section. Descriptive data, confirmation methods and algorithm use for included articles were coded. The primary objective of each study was reviewed to identify the primary measure of exposure, primary outcome and any additional covariates. A total of 168 articles met the inclusion criteria, the publication of which has increased in recent years. The majority used filtering algorithms (74%). The majority did not use observer and/or video confirmation for all sensor-recorded events (64%), which suggests estimates of head impact exposure from these studies may be imprecise.

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Biomechanical Studies of Impact and Helmet Protection

McIntosh¹

2021

Concussions in Athletics

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An assessment of the utility and functionality of wearable head impact sensors in Australian Football

Cited by 24 publications

References 20 publications

Video Analysis and Verification of Direct Head Impacts Recorded by Wearable Sensors in Junior Rugby League Players

Video Analysis and Verification of Direct Head Impacts Recorded by Wearable Sensors in Junior Rugby League Players

Head Impact Sensor Studies In Sports: A Systematic Review Of Exposure Confirmation Methods

Biomechanical Studies of Impact and Helmet Protection

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