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On-Field Deployment and Validation for Wearable Devices.

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Title: On-Field Deployment and Validation for Wearable Devices.
Authors: Kuo, Calvin1 (AUTHOR), Patton, Declan2 (AUTHOR), Rooks, Tyler3 (AUTHOR), Tierney, Gregory4 (AUTHOR), McIntosh, Andrew5,6,7 (AUTHOR), Lynall, Robert8 (AUTHOR), Esquivel, Amanda9 (AUTHOR), Daniel, Ray3 (AUTHOR), Kaminski, Thomas10 (AUTHOR), Mihalik, Jason11 (AUTHOR), Dau, Nate12 (AUTHOR), Urban, Jillian13 (AUTHOR) jurban@wakehealth.edu
Source: Annals of Biomedical Engineering. Nov2022, Vol. 50 Issue 11, p1372-1388. 17p.
Subjects: Wearable technology, Crash injuries, Sports injuries, Head injuries, Cephalometry, Military sports
Abstract: Wearable sensors are an important tool in the study of head acceleration events and head impact injuries in sporting and military activities. Recent advances in sensor technology have improved our understanding of head kinematics during on-field activities; however, proper utilization and interpretation of data from wearable devices requires careful implementation of best practices. The objective of this paper is to summarize minimum requirements and best practices for on-field deployment of wearable devices for the measurement of head acceleration events in vivo to ensure data evaluated are representative of real events and limitations are accurately defined. Best practices covered in this document include the definition of a verified head acceleration event, data windowing, video verification, advanced post-processing techniques, and on-field logistics, as determined through review of the literature and expert opinion. Careful use of best practices, with accurate acknowledgement of limitations, will allow research teams to ensure data evaluated is representative of real events, will improve the robustness of head acceleration event exposure studies, and generally improve the quality and validity of research into head impact injuries. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: On-Field Deployment and Validation for Wearable Devices.
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  Data: <searchLink fieldCode="AR" term="%22Kuo%2C+Calvin%22">Kuo, Calvin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Patton%2C+Declan%22">Patton, Declan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rooks%2C+Tyler%22">Rooks, Tyler</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tierney%2C+Gregory%22">Tierney, Gregory</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McIntosh%2C+Andrew%22">McIntosh, Andrew</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lynall%2C+Robert%22">Lynall, Robert</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Esquivel%2C+Amanda%22">Esquivel, Amanda</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Daniel%2C+Ray%22">Daniel, Ray</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kaminski%2C+Thomas%22">Kaminski, Thomas</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mihalik%2C+Jason%22">Mihalik, Jason</searchLink><relatesTo>11</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dau%2C+Nate%22">Dau, Nate</searchLink><relatesTo>12</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Urban%2C+Jillian%22">Urban, Jillian</searchLink><relatesTo>13</relatesTo> (AUTHOR)<i> jurban@wakehealth.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Biomedical+Engineering%22">Annals of Biomedical Engineering</searchLink>. Nov2022, Vol. 50 Issue 11, p1372-1388. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Wearable+technology%22">Wearable technology</searchLink><br /><searchLink fieldCode="DE" term="%22Crash+injuries%22">Crash injuries</searchLink><br /><searchLink fieldCode="DE" term="%22Sports+injuries%22">Sports injuries</searchLink><br /><searchLink fieldCode="DE" term="%22Head+injuries%22">Head injuries</searchLink><br /><searchLink fieldCode="DE" term="%22Cephalometry%22">Cephalometry</searchLink><br /><searchLink fieldCode="DE" term="%22Military+sports%22">Military sports</searchLink>
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  Data: Wearable sensors are an important tool in the study of head acceleration events and head impact injuries in sporting and military activities. Recent advances in sensor technology have improved our understanding of head kinematics during on-field activities; however, proper utilization and interpretation of data from wearable devices requires careful implementation of best practices. The objective of this paper is to summarize minimum requirements and best practices for on-field deployment of wearable devices for the measurement of head acceleration events in vivo to ensure data evaluated are representative of real events and limitations are accurately defined. Best practices covered in this document include the definition of a verified head acceleration event, data windowing, video verification, advanced post-processing techniques, and on-field logistics, as determined through review of the literature and expert opinion. Careful use of best practices, with accurate acknowledgement of limitations, will allow research teams to ensure data evaluated is representative of real events, will improve the robustness of head acceleration event exposure studies, and generally improve the quality and validity of research into head impact injuries. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Annals of Biomedical Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10439-022-03001-3
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      – SubjectFull: Wearable technology
        Type: general
      – SubjectFull: Crash injuries
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      – SubjectFull: Sports injuries
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              M: 11
              Text: Nov2022
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