Video Confirmation of Head Impact Sensor Data From High School Soccer Players

Abstract

Background: Recent advances in technology have enabled the development of head impact sensors, which provide a unique opportunity for sports medicine researchers to study head kinematics in contact sports. Studies have suggested that video or observer confirmation of head impact sensor data is required to remove false positives. In addition, manufacturer filtering algorithms may be ineffective in identifying true positives and removing true negatives. Purpose: To (1) identify the percentage of video-confirmed events recorded by headband-mounted sensors in high school soccer through video analysis, overall and by sex; (2) compare video-confirmed events with the classification by the manufacturer filtering algorithms; and (3) quantify and compare the kinematics of true- and false-positive events. Study Design: Cohort study; Level of evidence, 2. Methods: Adolescent female and male soccer teams were instrumented with headband-mounted impact sensors (SIM-G; Triax Technologies) during games over 2 seasons of suburban high school competition. Sensor data were sequentially reduced to remove events recorded outside of game times, associated with players not on the pitch (ie, field) and players outside the field of view of the camera. With video analysis, the remaining sensor-recorded events were identified as an impact event, trivial event, or nonevent. The mechanisms of impact events were identified. The classifications of sensor-recorded events by the SIM-G algorithm were analyzed. Results: A total of 6796 sensor events were recorded during scheduled varsity game times, of which 1893 (20%) were sensor-recorded events associated with players on the pitch in the field of view of the camera during verified game times. Most video-confirmed events were impact events (n = 1316, 70%), followed by trivial events (n = 396, 21%) and nonevents (n = 181, 10%). Female athletes had a significantly higher percentage of trivial events and nonevents with a significantly lower percentage of impact events. Most impact events were head-to-ball impacts (n = 1032, 78%), followed by player contact (n = 144, 11%) and falls (n = 129, 10%) with no significant differences between male and female teams. The SIM-G algorithm correctly identified 70%, 52%, and 66% of video-confirmed impact events, trivial events, and nonevents, respectively. Conclusion: Video confirmation is critical to the processing of head impact sensor data. Percentages of video-confirmed impact events, trivial events, and nonevents vary by sex in high school soccer. Current manufacturer filtering algorithms and magnitude thresholds are ineffective at correctly classifying sensor-recorded events and should be used with caution.

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

“…10,34,35,40,41 Recent technological advances have enabled the development of head impact sensors to estimate the head impact exposure of human subjects in vivo. 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.…”

“…47 Furthermore, field studies have evaluated the accuracy of sensors to measure head impact exposure using video analysis. 7,16,28,32,33 Such previous research has identified that the absence of observer and/or video confirmation of sensor-recorded events to remove false positives may result in substantial overestimation of head impact exposure. 7,18,32,33 Although filtering algorithms attempt to remove false positives in an automated fashion, some investigations suggest such approaches are limited and not a valid replacement for observer and/or video confirmation.…”

“…7,16,28,32,33 Such previous research has identified that the absence of observer and/or video confirmation of sensor-recorded events to remove false positives may result in substantial overestimation of head impact exposure. 7,18,32,33 Although filtering algorithms attempt to remove false positives in an automated fashion, some investigations suggest such approaches are limited and not a valid replacement for observer and/or video confirmation. 28,32,33 Therefore, the absence of rigorous confirmation methods or use of a filtering algorithm may result in estimates of head impact exposure that are imprecise.…”

“…7,18,32,33 Although filtering algorithms attempt to remove false positives in an automated fashion, some investigations suggest such approaches are limited and not a valid replacement for observer and/or video confirmation. 28,32,33 Therefore, the absence of rigorous confirmation methods or use of a filtering algorithm may result in estimates of head impact exposure that are imprecise. To understand the prevalence of this issue in the literature, the purpose of the current systematic review was to determine the proportion of head impact sensor data studies published in the last two decades that used filtering algorithms, observer confirmation and/or video confirmation of sensor-recorded events to remove false positives.…”

High Energy Side and Rear American Football Head Impacts Cause Obvious Performance Decrement on Video

“…30 In addition to low uncertainty kinematics estimates, qualitative video confirmation of head impacts has gained traction in the past few years as being very useful to pair with impact monitoring data, 6,8,14,17,[23][24][25]32,42,44,46,47,63 Bartsch et al, 4,5 Kieffer et al, 39 Bartsch et al, 3 Bartsch et al, 2 . 18,60,73 An emerging benefit of such video reviews is that objective visible signs (VS) such as motor incoordination, lying motionless, impact seizure, among others can be documented (Bruce et al BJSM 8 NHL).…”