A new open-access platform for measuring and sharing mTBI data
Abstract Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the...
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Nature Portfolio
2021
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oai:doaj.org-article:ef4d533bded0434b83dcbb4ffc9759932021-12-02T14:17:16ZA new open-access platform for measuring and sharing mTBI data10.1038/s41598-021-87085-22045-2322https://doaj.org/article/ef4d533bded0434b83dcbb4ffc9759932021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87085-2https://doaj.org/toc/2045-2322Abstract Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.August G. DomelSamuel J. RaymondChiara GiordanoYuzhe LiuSeyed Abdolmajid YousefsaniMichael FantonNicholas J. CecchiOlga VovkIleana PirozziAli KightBrett AveryAthanasia BoumisTyler FettersSimran JanduWilliam M. MehringSam MongaNicole MouchawarIndia RangelEli RicePritha RoySohrab SamiHeer SinghLyndia WuCalvin KuoMichael ZeinehGerald GrantDavid B. CamarilloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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DOAJ |
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DOAJ |
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EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q August G. Domel Samuel J. Raymond Chiara Giordano Yuzhe Liu Seyed Abdolmajid Yousefsani Michael Fanton Nicholas J. Cecchi Olga Vovk Ileana Pirozzi Ali Kight Brett Avery Athanasia Boumis Tyler Fetters Simran Jandu William M. Mehring Sam Monga Nicole Mouchawar India Rangel Eli Rice Pritha Roy Sohrab Sami Heer Singh Lyndia Wu Calvin Kuo Michael Zeineh Gerald Grant David B. Camarillo A new open-access platform for measuring and sharing mTBI data |
| description |
Abstract Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics. |
| format |
article |
| author |
August G. Domel Samuel J. Raymond Chiara Giordano Yuzhe Liu Seyed Abdolmajid Yousefsani Michael Fanton Nicholas J. Cecchi Olga Vovk Ileana Pirozzi Ali Kight Brett Avery Athanasia Boumis Tyler Fetters Simran Jandu William M. Mehring Sam Monga Nicole Mouchawar India Rangel Eli Rice Pritha Roy Sohrab Sami Heer Singh Lyndia Wu Calvin Kuo Michael Zeineh Gerald Grant David B. Camarillo |
| author_facet |
August G. Domel Samuel J. Raymond Chiara Giordano Yuzhe Liu Seyed Abdolmajid Yousefsani Michael Fanton Nicholas J. Cecchi Olga Vovk Ileana Pirozzi Ali Kight Brett Avery Athanasia Boumis Tyler Fetters Simran Jandu William M. Mehring Sam Monga Nicole Mouchawar India Rangel Eli Rice Pritha Roy Sohrab Sami Heer Singh Lyndia Wu Calvin Kuo Michael Zeineh Gerald Grant David B. Camarillo |
| author_sort |
August G. Domel |
| title |
A new open-access platform for measuring and sharing mTBI data |
| title_short |
A new open-access platform for measuring and sharing mTBI data |
| title_full |
A new open-access platform for measuring and sharing mTBI data |
| title_fullStr |
A new open-access platform for measuring and sharing mTBI data |
| title_full_unstemmed |
A new open-access platform for measuring and sharing mTBI data |
| title_sort |
new open-access platform for measuring and sharing mtbi data |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/ef4d533bded0434b83dcbb4ffc975993 |
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