Literature DB >> 35439140

Robotic Simulators for Tissue Examination Training with Multimodal Sensory Feedback.

Liang He, Perla Maiolino, Florence Leong, Thilina Lalitharatne, Simon De Lusignan, Ghajari Mazdak, Fumiya Iida, Thrishantha Nanayakkara.   

Abstract

Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.

Entities:  

Year:  2022        PMID: 35439140     DOI: 10.1109/RBME.2022.3168422

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  1 in total

1.  Editorial: Haptic training simulation, volume II.

Authors:  Xiaojun Chen; Arnaud Lelevé; Troy McDaniel; Carlos Rossa
Journal:  Front Robot AI       Date:  2022-09-08
  1 in total

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