Quick And Accurate Way To Detect Infant Pain
Infant pain detection, cry analysis, machine learning, audio processing, MFCC, Random Forest Classifier, real-time assessment.
Caregivers and healthcare professionals struggle to measure infants' suffering since they lack the ability to communicate it vocally. Conventional techniques frequently depend on subjective observation, which might cause delayed or erroneous results. This study presents a machine learning-based method that uses audio input to evaluate baby cries and identify the source of suffering, be it pain, hunger, or discomfort. The technology offers a dependable resource that helps parents and healthcare professionals by giving precise, real-time information into an infant's needs. In addition to improving pain detection accuracy, this objective approach gives caregivers the ability to act swiftly and properly. In contrast to conventional methods that rely on subjective observations, this system uses Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and a Random Forest Classifier for objective audio analysis. This approach provides real-time feedback, improves classification accuracy, and removes biases associated with caregiver interpretations, thereby enhancing the overall quality of care.
Volume 12
Issue 4,
April-2025
Pages : a238-a244
Paper Reg. ID: TIJER_157119
Published Paper Id: TIJER2504039
Downloads: 000282
Research Area: Science and Technology
Country: chennai, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
