• Media type: E-Article; Electronic Conference Proceeding
  • Title: EA-based smartwatch application for training and assistance in cardiopulmonary resuscitation
  • Contributor: Lins, Christian [Author]; Berwald, Erik [Author]; Klausen, Andreas [Author]; Hein, Andreas [Author]; Fudickar, Sebastian [Author]
  • Published: Association for Computing Machinery, 2023-07-24
  • Language: English
  • DOI: https://doi.org/20.500.12738/14370; https://doi.org/10.1145/3583133.3590752
  • ISBN: 9798400701207
  • Keywords: Computing methodologies ; Health informatics ; Genetic algorithms ; Applied computing
  • Origination:
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  • Description: Cardiopulmonary resuscitation (CPR) is critical to the prevention of death from cardiac arrest. Smartwatches can help first responders perform CPR correctly by collecting real-time acceleration data to determine compression rate and depth. We present an approach for a smartwatch application that uses motion data from the inertial sensors and an evolutionary algorithm ((μ + λ)-ES) to compute compression depth and frequency and provide corrective feedback. The application was optimized for out-of-hospital CPR and evaluated in a pilot study using a CPR training manikin as a reference system and an existing smartphone app for comparison. ; PeerReviewed