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Infants are exceptionally fragile and physically vulnerable. Continuous monitoring of their vital characteristics and physiological parameters is vital for assessing their exact health status. The progressive achievements of bioelectrical, biophysical, biochemical and hybrid soft monitoring technologies in infant are discussed. We offer insights into the remaining challenges and translational hurdles of soft electronics for clinical-grade infant monitoring from the benchtop to the bedside. [Display omitted] Infants are exceptionally fragile and physically vulnerable who need special healthcare. Continuous monitoring of vital information of infants is essential for understanding their health status. Whereas modern infant health monitoring is carried out with dedicated facilities in controlled hospital settings, they are associated with several issues such as potential iatrogenic injuries, barriers for skin-to-skin parental bonding, and even complications during basic clinical tasks. Recently, soft electronics have become an accessory and mobile infant monitoring platform with remarkable advantages over existing technologies. This review comprehensively summarizes the research advances on soft electronics for advanced infant monitoring. We first introduce the physiological characteristics from anatomy, diseases, and treatments of infants. In accordance with these characteristics, we discuss the progressive achievements of soft bioelectrical, biophysical, biochemical, and hybrid monitoring technologies for infants. In addition, we offer insights into the remaining challenges and future directions of a soft electronics based intelligent system for clinical-grade infant monitoring from sensor design, data processing, power supply, and digital interactions in the era of Internet of Things. Finally, we highlight the translational challenges to be considered in bringing soft electronics for infant monitoring from the benchtop to the bedside. [ABSTRACT FROM AUTHOR]

In order to improve the welfare of newborns, this study investigates the use of sound-recognition-based artificial intelligence (AI) approaches to the interpretation and monitoring of infant screams. Crying has long been a problem because it is the primary means of communication between infants and caregivers. The limitations of conventional interpretation techniques are discussed. These limitations include the subjective nature of interpretation and the inability to detect subtle variations in crying patterns. The goal of the research is to categorize crying patterns based on the cries of male and female infants and identify noises that are a sign of distress. The study utilized the Mel Frequency Cepstral Coefficients (MFCC) method to extract features from internet-sourced MP3 and WAV audio data. The technique successfully captured the unique qualities of each crying sound using various machine-learning models, including Random Forest and XGBoost. These models outperformed others with accuracy rates of 94.5% and 94.2%, respectively. These findings show how well these algorithms perform in correctly categorizing various newborn cries. The findings of this study establish the platform for possible Internet of Things (IoT) and healthcare framework implementations targeted at supporting parents in caring for their newborns by offering an insightful understanding of the distinctive vocalizations connected with weeping.

Cry is an important signal in early infancy for parents to understand needs of their baby and thereby to provide timely parenting/soothing or to be reassured. Thanks to the recent advancement of signal processing, deep learning, and internet-of-things technologies, smart baby monitors with a microphone and/or a video camera have attracted a lot of attention to be used in a baby room to assist parental activities. In this paper, we propose a two-step approach to detect infant cries automatically with continuous audio signals. We first identify and remove the segments without clear sounds (background noise) using a volume-based thresholding algorithm, followed by convolutional neural network (CNN) models to further detect infant cries. The CNN operates on the log linear-scale filterbank energies of audio signals to extract features for cry detection. In this study, a large set of audio data (151.8 hours) collected from five infants in home settings were included. Our proposed approach achieved a mean accuracy of 98.6% in identifying background noise (with only 2 out of 3209 cry segments missed) and a mean accuracy of 92.2% in detecting cries from other non-background sounds. [ABSTRACT FROM AUTHOR]

This article presents an indoor infant monitoring system based on Android system which employs a low-cost Bluetooth Low Energy (BLE) pseudolite to accurately locate the infants in the activity indoors and timely remind the parents. It can achieve a positioning accuracy better than 2 m and effectively reduce the risk of injury to infants and young children. The performance of the proposed system, using false negative rate, false alarm rate, and alarm response time as indicators, is compared with that of the system using Quuppa as a positioning source. The experiment results show that both BLE pseudolite and Quuppa as a positioning source can basically meet the positioning accuracy for the infant monitoring system, but the BLE-pseudolite-based system provides a more cost-efficient solution.

The manual monitoring of young infants suffering from diseases like reflux is significant, since infants can hardly articulate their feelings. In this work, we propose a video-based infant monitoring system for the analysis of infant expressions and states, approaching real-time performance. The expressions of interest consist of discomfort, unhappy, joy and neutral, whereas states include sleep, pacifier and open mouth. Benefiting from the expression analysis, the discomfort moments can also be used and correlated with a symptom-related disease, such as a reflux measurement for the diagnosis of gastroesophageal reflux. The system consists of three components: infant expressions and states detection, object tracking and detection compensation. The proposed system is based on combining expression detection using Fast R-CNN with a compensated detection using analyzing information from the previous frame and utilizing a Hidden Markov Model. The experimental results show a mean average precision of 81.9% and 84.8% for 4 infant expressions and 3 states evaluated with both clinical and daily datasets. Meanwhile, the average precision for discomfort detection achieves up to 90%. [ABSTRACT FROM AUTHOR]

This paper presents the first multimodal neonatal pain dataset that contains visual, vocal, and physiological responses following clinically required procedural and postoperative painful procedures. It was collected from 58 neonates (27-41 gestational age) during their hospitalization in the neonatal intensive care unit. The visual and vocal data were recorded using an inexpensive RGB camera while the physiological responses (vital signs and cortical activity) were recorded using portable bedside monitors. The recorded behavioral and physiological responses were scored by expert nurses using two validated pain scales to obtain the ground truth labels. In addition to behavioral and physiological responses, our dataset contains clinical information such as the neonate’s age, gender, weight, pharmacological and non-pharmacological interventions, and previous painful procedures. The presented multimodal dataset can be used to develop artificial intelligence systems that monitor, assess, and predict neonatal pain based on the analysis of behavioral and physiological responses. It can also be used to advance the understanding of neonatal pain, which can lead to the development of effective pain prevention and treatment.

Infant monitoring is very important in daily life, since infants lack the ability to verbalize and communicate with their caregivers. However, a full-time monitoring provided by caregivers or practitioners is very expensive and labor intensive. To solve the practical difficulties for particular nighttime monitoring and further reduce the labor cost, in this article, we propose a novel 24-hour infant monitoring system, enabling continuous monitoring for infant discomfort detection using both Red-Green-Blue (RGB) and Near Infrared-light (NIR) videos. The proposed algorithm is robust to arbitrary head rotations, occlusions and face profiles. For this purpose, a Faster R-CNN architecture is first pre-trained with the ImageNet dataset, and then fine-tuned with a specific training dataset of different infant expressions. Our proposed method obtains an Average Precision of more than 87% for classifying discomfort expression. Infant monitoring during the nighttime using NIR videos is proposed for the first time, and the presented system enables reflux disease analysis and continuous remote home monitoring in a more relaxed environment, which is largely preferred by pediatricians and parents. [ABSTRACT FROM AUTHOR]

This article presents an indoor infant monitoring system based on Android system which employs a low-cost Bluetooth Low Energy (BLE) pseudolite to accurately locate the infants in the activity indoors and timely remind the parents. It can achieve a positioning accuracy better than 2 m and effectively reduce the risk of injury to infants and young children. The performance of the proposed system, using false negative rate, false alarm rate, and alarm response time as indicators, is compared with that of the system using Quuppa as a positioning source. The experiment results show that both BLE pseudolite and Quuppa as a positioning source can basically meet the positioning accuracy for the infant monitoring system, but the BLE-pseudolite-based system provides a more cost-efficient solution. [ABSTRACT FROM AUTHOR]

Prolonged monitoring of infant sleep is paramount for parents and healthcare professionals for interpreting and evaluating infants’ sleep quality. Wake-sleep patterns are often studied to assess this. Video cameras have received a lot of attention in infant sleep monitoring because they are unobtrusive and easy to use at home. In this paper, we propose a method using motion data detected from infrared video frames (video-based actigraphy) to identify wake and sleep states. The motion, mostly caused by infant body movement, is known to be substantially associated with infant wake and sleep states. Two features were calculated from the video-based actigraphy, and a Bayesian-based linear discriminant classification model was employed to classify the two states. Leave-one-subject-out cross validation was performed to validate our proposed wake and sleep classification model. From a total of 11.6 h of infrared video recordings of 10 healthy term infants in a laboratory pilot study, we achieved a reliable classification performance with a Cohen’s kappa coefficient of 0.733 ± 0.204 (mean ± standard deviation) and an overall accuracy of 92.0% ± 4.6%.

Abstract: The breathing act is usually quite noticeable in most vertebrates; hence, the measurement of breathing frequency (f) rarely poses a serious problem. Differently, the quantitative assessment of tidal volume (V T) (from which air ventilation, is computed) can be a major challenge. This article reviews the most common experimental approaches to quantify V T in adult or young vertebrates of small body size. In these animals, techniques commonly used in adult humans are unsuitable. Furthermore, physiologically meaningful data necessitate techniques with minimal disturbance to the subject under investigation. During the last fifty years numerous and ingenious approaches have been developed and refined. Although none of them can be considered ideal or totally error-free, for specific tasks and/or species there is an optimal approach to measure tidal volume. [Copyright &y& Elsevier]

The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and discontinuous. To develop a reliable medical interpretation, several automated approaches have been proposed to enhance the current practice. These approaches are unimodal and focus mainly on assessing neonatal procedural (acute) pain. As pain is a multimodal emotion that is often expressed through multiple modalities, the multimodal assessment of pain is necessary especially in case of postoperative (acute prolonged) pain. Additionally, spatio-temporal analysis is more stable over time and has been proven to be highly effective at minimizing misclassification errors. In this paper, we present a novel multimodal spatio-temporal approach that integrates visual and vocal signals and uses them for assessing neonatal postoperative pain. We conduct comprehensive experiments to investigate the effectiveness of the proposed approach. We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration. The experimental results, on a real-world dataset, show that the proposed multimodal spatio-temporal approach achieves the highest AUC (0.87) and accuracy (79%), which are on average 6.67% and 6.33% higher than unimodal approaches. The results also show that the integration of temporal information markedly improves the performance as compared to the non-temporal approach as it captures changes in the pain dynamic. These results demonstrate that the proposed approach can be used as a viable alternative to manual assessment, which would tread a path toward fully automated pain monitoring in clinical settings, point-of-care testing, and homes., (Copyright © 2020 Elsevier Ltd. All rights reserved.)