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13 pages, 3377 KiB

Open AccessArticle

Development of a Baby Cry Identification System Using a Raspberry Pi-Based Embedded System and Machine Learning

by Mohcin Mekhfioui, Wiam Fadel, Fatima Ezzahra Hammouch, Oussama Laayati, Marouan Bouchouirbat, Nabil El Bazi, Amal Satif, Tarik Boujiha and Ahmed Chebak

Technologies 2025, 13(4), 130; https://doi.org/10.3390/technologies13040130 - 31 Mar 2025

Viewed by 1426

Abstract

Newborns cry intensely, and most parents struggle to understand the reason behind their crying, as the baby cannot verbally express their needs. This makes it challenging for parents to know if their child has a need or a health issue. An embedded solution based on a Raspberry Pi is presented to address this problem. The module analyzes audio techniques to capture, analyze, classify, and remotely monitor a baby’s cries. These techniques rely on prosodic and cepstral features, such as MFCC coefficients. They can differentiate the reason behind a baby’s cry, such as hunger, stomach pain, or discomfort. A machine learning model was trained to anticipate the reason based on audio features. The embedded system includes a microphone to capture real-time cries and a display screen to show the anticipated reason. In addition, the system sends the collected data to a web server for storage, enabling remote monitoring and more detailed data analysis. A cell phone application has also been developed to notify parents in real time of why their baby is crying. This application enables parents to adapt quickly and efficiently to their infant’s needs, even when they are not around. Full article

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10 pages, 1344 KiB

Open AccessArticle

Deep Learning for Infant Cry Recognition

by Yun-Chia Liang, Iven Wijaya, Ming-Tao Yang, Josue Rodolfo Cuevas Juarez and Hou-Tai Chang

Int. J. Environ. Res. Public Health 2022, 19(10), 6311; https://doi.org/10.3390/ijerph19106311 - 23 May 2022

Cited by 32 | Viewed by 5026

Abstract

Recognizing why an infant cries is challenging as babies cannot communicate verbally with others to express their wishes or needs. This leads to difficulties for parents in identifying the needs and the health of their infants. This study used deep learning (DL) algorithms such as the convolutional neural network (CNN) and long short-term memory (LSTM) to recognize infants’ necessities such as hunger/thirst, need for a diaper change, emotional needs (e.g., need for touch/holding), and pain caused by medical treatment (e.g., injection). The classical artificial neural network (ANN) was also used for comparison. The inputs of ANN, CNN, and LSTM were the features extracted from 1607 10 s audio recordings of infants using mel-frequency cepstral coefficients (MFCC). Results showed that CNN and LSTM both provided decent performance, around 95% in accuracy, precision, and recall, in differentiating healthy and sick infants. For recognizing infants’ specific needs, CNN reached up to 60% accuracy, outperforming LSTM and ANN in almost all measures. These results could be applied as indicators for future applications to help parents understand their infant’s condition and needs. Full article

(This article belongs to the Special Issue 2nd Edition of Big Data, Decision Models, and Public Health)

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15 pages, 13973 KiB

Open AccessArticle

Implementation of Automated Baby Monitoring: CCBeBe

by Soohyun Choi, Songho Yun and Byeongtae Ahn

Sustainability 2020, 12(6), 2513; https://doi.org/10.3390/su12062513 - 23 Mar 2020

Cited by 10 | Viewed by 5944

Abstract

An automated baby monitoring service CCBeBe (CCtv Bebe) monitors infants’ lying posture and crying based on AI and provides parents-to-baby video streaming and voice transmission. Besides, parents can get a three-minute daily video diary made by detecting the baby’s emotion such as happiness. These main features are based on OpenPose, EfficientNet, WebRTC, and Facial-Expression-Recognition.Pytorch. The service is integrated into an Android application and works on two paired smartphones, with lowered hardware dependence. Full article

(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)

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16 pages, 1483 KiB

Open AccessArticle

An Arduino-Based Resonant Cradle Design with Infant Cries Recognition

by Chun-Tang Chao, Chia-Wei Wang, Juing-Shian Chiou and Chi-Jo Wang

Sensors 2015, 15(8), 18934-18949; https://doi.org/10.3390/s150818934 - 3 Aug 2015

Cited by 18 | Viewed by 9679

Abstract

This paper proposes a resonant electric cradle design with infant cries recognition, employing an Arduino UNO as the core processor. For most commercially available electric cradles, the drive motor is closely combined with the bearing on the top, resulting in a lot of energy consumption. In this proposal, a ball bearing design was adopted and the driving force is under the cradle to increase the distance from the object to fulcrum and torque. The sensors are designed to detect the oscillation state, and then the force is driven at the critical time to achieve the maximum output response while saving energy according to the principle of resonance. As for the driving forces, the winding power and motors are carefully placed under the cradle. The sensors, including the three-axis accelerometer and infrared sensor, are tested and applied under swinging amplitude control. In addition, infant cry recognition technology was incorporated in the design to further develop its functionality, which is a rare feature in this kind of hardware. The proposed nonlinear operator of fundamental frequency ( ) analysis is able to identify different types of infant cries. In conclusion, this paper proposes an energy-saving electric cradle with infant cries recognition and the experimental results demonstrate the effectiveness of this approach. Full article

(This article belongs to the Section Physical Sensors)

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