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Accepted for/Published in: JMIR Human Factors

Date Submitted: Feb 19, 2025
Open Peer Review Period: Feb 17, 2025 - Apr 14, 2025
Date Accepted: Oct 16, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

On the Design of a Sign Language Corpus of Medical Terms for Automatic Translation Systems: Mixed Methods Approach

Marcolino MS, Rosa LMMS, Bernardino ELA, Campos MFM

On the Design of a Sign Language Corpus of Medical Terms for Automatic Translation Systems: Mixed Methods Approach

JMIR Hum Factors 2026;13:e72789

DOI: 10.2196/72789

PMID: 42054555

On the design of a sign language corpus of medical terms for automatic translation systems: a mixed-methods approach

  • Milena Soriano Marcolino; 
  • Luiza Marinho Motta Santa Rosa; 
  • Elidea Lucia Almeida Bernardino; 
  • Mario Fernando Montenegro Campos

ABSTRACT

Background:

Hearing loss is a global health issue affecting millions and creating significant communication barriers, particularly in accessing healthcare services. These barriers can lead to complications and iatrogenic events, emphasizing the need for assistive technologies that enhance communication efficiency.

Objective:

To develop a corpus of medical terms for the "Captar-Libras" project, designed to improve communication between healthcare professionals and deaf patients through a bidirectional sign language system.

Methods:

This study used the Delphi method to obtain consensus on key terms for a sign language translation system in healthcare emergency consultations. Initially, a questionnaire with common emergency questions was developed and distributed to healthcare professionals. The collected data were analyzed by a team of experts and adapted to Brazilian Sign Language (Libras). Simulated clinical scenarios were then created to validate the system and ensure the vocabulary's accuracy in the medical context.

Results:

Among the 16 participants, most were physicians (87.5%) with experience in emergency care, and half had previously treated patients with hearing loss in emergency settings. The questions evaluated received high average importance scores, particularly those related to initial symptoms and pain intensity. Some suggestions for adjustments were made, with two wording modifications significantly improving clarity regarding smoking and alcohol use. Additional suggestions to enhance the medical interview were also proposed. This study aimed to identify essential questions for emergency consultations with deaf patients, focusing on developing a corpus for a Brazilian Sign Language (Libras) recognition system. The findings emphasize the importance of effective communication and highlight the challenges of translating medical terms into Libras. To address these complexities, a multidisciplinary team used the Delphi method to ensure linguistic and cultural accuracy. Additionally, the study reinforces the need for clear, structured medical queries to improve accessibility in emergency care. As a next step, system validation through simulated scenarios will be conducted. Despite certain limitations, this research lays a solid foundation for advancing sign language recognition in medical settings.

Conclusions:

This study represents a step forward in improving communication between healthcare professionals and deaf individuals in emergencies, where accuracy and overcoming translation challenges are essential. The development of a structured corpus of medical terms in Brazilian Sign Language (Libras) enhances accessibility and inclusivity. Future validation will focus on assessing the recognition system’s performance and reliability in real-world scenarios.


 Citation

Please cite as:

Marcolino MS, Rosa LMMS, Bernardino ELA, Campos MFM

On the Design of a Sign Language Corpus of Medical Terms for Automatic Translation Systems: Mixed Methods Approach

JMIR Hum Factors 2026;13:e72789

DOI: 10.2196/72789

PMID: 42054555

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