Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 23, 2025

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.3K

Automating medical simulations.

Sapir Gershov1, Daniel Braunold2, Robert Spektor3

  • 1Technion Autonomous Systems Program, Technion - Israel Institute of Technology, Haifa, Israel.

Journal of Biomedical Informatics
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

6-DoF dental pose estimation for AR-assisted craniofacial surgery.

International journal of computer assisted radiology and surgery·2026
Same author

An externally validated machine learning algorithm for predicting mental and physical health outcomes three months post-hospitalization for severe viral infection with SARS-CoV-2.

Brain, behavior, & immunity - health·2026
Same author

Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.

Journal of medical Internet research·2026
Same author

Machine Learning-Based Predictive Model for Fever and Adverse Clinical Events in Hospitalized Pediatric Burn Patients.

Journal of burn care & research : official publication of the American Burn Association·2026
Same author

Current validation practice undermines surgical AI development.

ArXiv·2026
Same author

Postpartum headache after epidural analgesia: A case of overlapping post-dural puncture headache, pneumocephalus, posterior reversible encephalopathy syndrome and intracranial hemorrhage.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026
Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026
See all related articles

This study introduces a speech-based framework for human activity recognition (HAR) in medical settings, outperforming expert examiners. This technology enhances workflow efficiency and patient safety by automatically validating checklists.

Area of Science:

  • Medical Informatics
  • Speech Processing
  • Human Activity Recognition

Background:

  • Current human activity recognition (HAR) technologies in medical settings face limitations due to privacy concerns, environmental constraints, and interference from medical personnel.
  • Existing HAR modalities like video and sensors are often unsuitable for sensitive medical environments.

Purpose of the Study:

  • To explore speech as an alternative modality for HAR in medical settings.
  • To develop an automated objective checklist validation framework using speech recognition.

Main Methods:

  • An end-to-end framework was developed to record, process, and analyze medical personnel's speech.
  • Speech data was utilized to automatically recognize and document actions performed within a checklist format.
Keywords:
Activity RecognitionChecklistMachine LearningMedicalMedical SimulationNLPSpeech

More Related Videos

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.5K
Mechanical Ventilation Boot Camp Curriculum
07:36

Mechanical Ventilation Boot Camp Curriculum

Published on: March 12, 2018

10.3K

Related Experiment Videos

Last Updated: Jul 23, 2025

Emergency Undocking in Robotic Surgery: A Simulation Curriculum
06:48

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

9.3K
Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training
09:15

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Published on: February 10, 2022

3.5K
Mechanical Ventilation Boot Camp Curriculum
07:36

Mechanical Ventilation Boot Camp Curriculum

Published on: March 12, 2018

10.3K

Main Results:

  • The speech-based HAR framework achieved an F1 score of 0.869 on verbal tasks, outperforming an online expert examiner.
  • An intraclass correlation (ICC) score of 0.822 was obtained with an offline examiner, indicating high reliability.
  • The framework successfully identified communication failures and medical errors.

Conclusions:

  • Speech-based HAR offers a promising solution to overcome limitations of current technologies in medical environments.
  • This framework can enhance workflow efficiency, patient safety, and support the development of automated assistive technologies.
  • Potential applications include emergency rooms and operating rooms, improving care delivery.