Automated Heart and Lung Auscultation in Robotic Physical Examinations

Yifan Zhu, Alexander Smith, Kris Hauser

Research output: Contribution to journalArticlepeer-review


This letter presents the first implementation of autonomous robotic auscultation of heart and lung sounds. To select auscultation locations that generate high-quality sounds, a Bayesian Optimization (BO) formulation leverages visual anatomical cues to predict where high-quality sounds might be located, while using auditory feedback to adapt to patient-specific anatomical qualities. Sound quality is estimated online using machine learning models trained on a database of heart and lung stethoscope recordings. Experiments on 4 human subjects show that our system autonomously captures heart and lung sounds of similar quality compared to tele-operation by a human trained in clinical auscultation. Surprisingly, one of the subjects exhibited a previously unknown cardiac pathology that was first identified using our robot, which demonstrates the potential utility of autonomous robotic auscultation for health screening.

Original languageEnglish (US)
Pages (from-to)4204-4211
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 1 2022
Externally publishedYes


  • Telerobotics and teleoperation
  • medical robots and systems
  • planning under uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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