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Cross-Modal Characterization of Infant Cry: Validation of a Chest-Surface Accelerometer in Extracting Acoustic Vocal
Winko W An1, Saketh Sundar2, Lisa Yankowitz3
1Division of Developmental Medicine, Boston Children's Hospital, Boston, MA 02115 USA, and also with Harvard Medical School, Boston, MA 02115 USA.
Arxiv
|June 5, 2026
Summary
Chest accelerometers reliably capture infant cry acoustics, offering a noise-robust and private alternative to microphones. This method shows promise for early neurodevelopmental disorder biomarkers.
Area of Science:
- Neurodevelopmental research
- Biomarker discovery
- Infant vocalizations
Background:
- Infant cry acoustics offer insights into early neurodevelopment and potential biomarkers for neurodevelopmental disorders.
- Microphone recordings are limited by environmental noise and privacy concerns.
- Chest-surface accelerometers present a robust, privacy-preserving alternative for capturing laryngeal vibrations.
Purpose of the Study:
- To evaluate the validity of chest-mounted accelerometers for infant cry analysis.
- To compare acoustic features from accelerometer and microphone recordings.
- To assess the potential of accelerometers as a scalable clinical tool.
Main Methods:
- Compared acoustic features from chest-mounted accelerometers (ACC) and microphones (MIC) in 85 infants.
- Extracted seven vocal measures: fundamental frequency (F0), jitter, shimmer, cepstral peak prominence (CPP), and harmonics-to-noise ratio (HNR).
- Assessed agreement using intraclass correlation coefficients (ICCs).
Main Results:
- Excellent agreement (ICC > 0.94) for F0 between ACC and MIC.
- Good-to-excellent agreement for jitter; moderate agreement for CPP.
- Lower agreement and bias for shimmer and HNR, indicating potential differences in signal transmission.
Conclusions:
- Chest accelerometers reliably capture key infant cry acoustic features like F0 and jitter.
- This technology provides a noise-robust, privacy-preserving alternative to traditional microphones.
- Accelerometer-based infant cry analysis holds potential for scalable clinical and developmental research.
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