A Systematic Review of Advances in Infant Cry Paralinguistic Classification: Methods, Implementation, and Applications.

Q2 Medicine
Geofrey Owino, Bernard Bernard Shibwabo
{"title":"A Systematic Review of Advances in Infant Cry Paralinguistic Classification: Methods, Implementation, and Applications.","authors":"Geofrey Owino, Bernard Bernard Shibwabo","doi":"10.2196/69457","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Effective communication is essential for human interaction, yet infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow leaving gaps in timely, precise caregiving responses. A precise interpretation of infant cries can potentially provide valuable insights into the infant's health, needs, and well-being, enabling prompt medical or caregiving actions.</p><p><strong>Objective: </strong>This study seeks to systematically review the advancements in methods, coverage, deployment schemes, and applications of infant cry classification over the last 24 years. The review focuses on the different infant cry classification techniques, feature extraction methods, and the practical applications. Furthermore, we aimed to identify recent trends and directions in the field of infant cry signal processing to address both academic and practical needs.</p><p><strong>Methods: </strong>A systematic literature review was conducted by using nine electronic databases: Cochrane Database of Systematic Reviews, JSTOR, Web of Science Core Collection, Scopus, PubMed, ACM, MEDLINE, IEEE Xplore, and Google Scholar. A total of 5904 search results were initially retrieved, with 126 studies meeting the eligibility criteria after screening by two independent reviewers. The methodological quality of the studies was assessed using the Cochrane risk-of-bias tool version 2 (RoB2), with 92% (n=116) of the studies indicating a low risk of bias and 8% (n=10) of the studies showing some concerns regarding bias. The overall quality assessment was performed using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. The data analysis was conducted using R version 3.64.</p><p><strong>Results: </strong>Notable advancements in infant cry classification methods were realized, particularly from 2019 onwards employing machine learning, deep learning, and hybrid approaches. Common audio features included Mel-frequency cepstral coefficients (MFCCs), spectrograms, pitch, duration, intensity, formants, zero-crossing rate and chroma. Deployment methods included mobile applications and web-based platforms for real-time analysis with 90% (n=113) of the remaining models remained undeployed to real world applications. Denoising techniques and federated learning were limitedly employed to enhance model robustness and ensure data confidentiality from 5% (n=6) of the studies. Some of the practical applications spanned healthcare monitoring, diagnostics, and caregiver support.</p><p><strong>Conclusions: </strong>The evolution of infant cry classification methods has progressed from traditional classical statistical methods to machine learning models but with minimal considerations of data privacy, confidentiality, and ultimate deployment to the practical use. Further research is thus proposed to develop standardized foundational audio multimodal approaches, incorporating a broader range of audio features and ensuring data confidentiality through methods such as federated learning. Furthermore, a preliminary layer is proposed for denoising the cry signal before the feature extractions stage. These improvements will enhance the accuracy, generalizability, and practical applicability of infant cry classification models in diverse healthcare settings.</p>","PeriodicalId":36224,"journal":{"name":"JMIR Rehabilitation and Assistive Technologies","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Rehabilitation and Assistive Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/69457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Effective communication is essential for human interaction, yet infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow leaving gaps in timely, precise caregiving responses. A precise interpretation of infant cries can potentially provide valuable insights into the infant's health, needs, and well-being, enabling prompt medical or caregiving actions.

Objective: This study seeks to systematically review the advancements in methods, coverage, deployment schemes, and applications of infant cry classification over the last 24 years. The review focuses on the different infant cry classification techniques, feature extraction methods, and the practical applications. Furthermore, we aimed to identify recent trends and directions in the field of infant cry signal processing to address both academic and practical needs.

Methods: A systematic literature review was conducted by using nine electronic databases: Cochrane Database of Systematic Reviews, JSTOR, Web of Science Core Collection, Scopus, PubMed, ACM, MEDLINE, IEEE Xplore, and Google Scholar. A total of 5904 search results were initially retrieved, with 126 studies meeting the eligibility criteria after screening by two independent reviewers. The methodological quality of the studies was assessed using the Cochrane risk-of-bias tool version 2 (RoB2), with 92% (n=116) of the studies indicating a low risk of bias and 8% (n=10) of the studies showing some concerns regarding bias. The overall quality assessment was performed using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. The data analysis was conducted using R version 3.64.

Results: Notable advancements in infant cry classification methods were realized, particularly from 2019 onwards employing machine learning, deep learning, and hybrid approaches. Common audio features included Mel-frequency cepstral coefficients (MFCCs), spectrograms, pitch, duration, intensity, formants, zero-crossing rate and chroma. Deployment methods included mobile applications and web-based platforms for real-time analysis with 90% (n=113) of the remaining models remained undeployed to real world applications. Denoising techniques and federated learning were limitedly employed to enhance model robustness and ensure data confidentiality from 5% (n=6) of the studies. Some of the practical applications spanned healthcare monitoring, diagnostics, and caregiver support.

Conclusions: The evolution of infant cry classification methods has progressed from traditional classical statistical methods to machine learning models but with minimal considerations of data privacy, confidentiality, and ultimate deployment to the practical use. Further research is thus proposed to develop standardized foundational audio multimodal approaches, incorporating a broader range of audio features and ensuring data confidentiality through methods such as federated learning. Furthermore, a preliminary layer is proposed for denoising the cry signal before the feature extractions stage. These improvements will enhance the accuracy, generalizability, and practical applicability of infant cry classification models in diverse healthcare settings.

求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.20
自引率
0.00%
发文量
31
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信