Development of a novel cardiopulmonary resuscitation measurement tool using real-time feedback from wearable wireless instrumentation.
Aim: The design and implementation of a wearable training device to improve cardiopulmonary resuscitation (CPR) is presented.Methods: The MYO contains both Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors which are used to detect effective CPR, and the four common incorrect hand an...
| Publicado en: | Resuscitation Vol. 137; pp. 183 - 190 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135712051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135712051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03009572 3PQ jtl: Resuscitation issn: 03009572 maglogo: N pubinfo: dt: Apr2019 vid: 137 pid: 1004 pub: Elsevier B.V. artinfo: ui: 135712051 135712051 NLM30797861 135712051 10.1016/j.resuscitation.2019.02.019 NLM30797861 135712051 ppf: 183 ppct: 7 formats: tig: atl: Development of a novel cardiopulmonary resuscitation measurement tool using real-time feedback from wearable wireless instrumentation. aug: au: Ward, Sarah R. Scott, Bronwyn C. Rubin, David M. Pantanowitz, Adam affil: Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Johannesburg, South Africa sug: subj: Wireless Communications Resuscitation, Cardiopulmonary Feedback Human Electromyography Models, Anatomic Equipment Design User-Computer Interface Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Aim: The design and implementation of a wearable training device to improve cardiopulmonary resuscitation (CPR) is presented.Methods: The MYO contains both Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors which are used to detect effective CPR, and the four common incorrect hand and arm positions viz. relaxed fingers; hands too low on the sternum; patient too close; or patient too far. The device determines the rate and depth of compressions calculated using a Fourier transform and dual-quaternions respectively. In addition, common positional mistakes are determined using classification algorithms (six machine learning algorithms are considered and tested). Feedback via Graphical User Interface (GUI) and audio is integrated.Results: The system is tested by performing CPR on a mannequin and comparing real-time results to theoretical values. Tests show that although the classification algorithm performed well in testing (98%), in real time, it had low accuracy for certain categories (60%), which are attributable to the MYO calibration, sampling rate and misclassification of similar hand positions. Combining these similar incorrect positions into more general categories significantly improves accuracy, and produces the same improved outcome of improved CPR. The rate and depth measures have a general accuracy of 97%.Conclusion: The system allows for portable, real-time feedback for use in training and in the field, and shows promise toward classifying and improving the administration of CPR. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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