Background: In smart cities, prioritizing child safety through affordable technology like the Internet of Things (IoT) is crucial for parents. This study seeks to investigate different IoT tools that can prevent and address accidents involving children. The goal is to alleviate the emotional and financial toll of such incidents due to their high mortality rates.
Methods: This study considers articles published in English that use IoT for children's healthcare. PubMed, Science Direct, and Web of Science databases are considered as searchable databases. 273 studies were retrieved after the initial search. After eliminating duplicate records, studies were assessed based on input and output criteria. Titles and abstracts were reviewed for relevance. Articles not meeting criteria were excluded. Finally, 29 cases had the necessary criteria to enter this study.
Results: The study reveals that India is at the forefront of IoT research for children, followed by Italy and China. Studies mainly occur indoors, utilizing wearable sensors like heart rate, motion, and tracking sensors. Biosignal sensors and technologies such as Zigbee and image recognition are commonly used for data collection and analysis. Diverse approaches, including cloud computing and machine vision, are applied in this innovative field.
Conclusions: In conclusion, IoT for children is mainly seen in developed countries like India, Italy, and China. Studies focus on indoor use, using wearable sensors for heart rate monitoring. Biosignal sensors and various technologies like Zigbee, Kinect, image recognition, RFID, and robots contribute to enhancing children's well-being.
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http://dx.doi.org/10.4103/ijpvm.ijpvm_191_23 | DOI Listing |
J Phys Condens Matter
March 2025
Zhengzhou University, kexue dadao No.100,zhengzhou, Zhengzhou, 450001, CHINA.
The depletion of fossil fuels and the environmental impact of chemical batteries, coupled with the rapid proliferation of portable electronic devices and the Internet of Things (IoT), have created an urgent demand for high-performance, lightweight, and sustainable energy systems. Flexible triboelectric nanogenerators (TENGs) have emerged as a promising technology for powering self-sufficient devices, offering advantages such as simple structure, flexibility, low cost, and environmental adaptability. In particular, electrospun nanofiber-based TENGs stand out due to their enhanced surface area, superior charge collection capabilities, and improved mechanical durability.
View Article and Find Full Text PDFNetwork
March 2025
Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, India.
The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done.
View Article and Find Full Text PDFSci Rep
March 2025
School of Material Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.
Oil-based drilling cutting residues (OBDCRs) are among the primary solid wastes generated during shale gas exploration and development. Utilizing existing equipment to transform OBDCRs into ceramsites appears to be a feasible and resource-efficient approach. In this study, building ceramsites were prepared with OBDCRs incorporating with fly ash (a byproduct of coal combustion) as raw materials.
View Article and Find Full Text PDFISA Trans
March 2025
School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide SA 5005, Australia; Research and Innovation Centre, Obuda University, 1034 Budapest, Hungary. Electronic address:
This paper develops a novel temporal difference Q (TD-Q) learning approach, designed to address the robust control challenge in discrete-time Markov jump systems (MJSs) which are characterized by entirely unknown dynamics and transition probabilities (TPs). The model-free TD-Q learning method is uniquely comprehensive, including two special cases: Q learning for MJSs with unknown dynamics, and TD learning for MJSs with undetermined TPs. We propose an innovative ternary policy iteration framework, which iteratively refines the control policies through a dynamic loop of alternating updates.
View Article and Find Full Text PDFJ Environ Manage
March 2025
School of Digital Industry, Jimei University, Xiamen, 361021c, China; Xiamen Jianpan Kunlu Internet of Things Research Institute, Xiamen, 361021, China. Electronic address:
This study investigates the crucial relationship between renewable energy consumption and digitization to drive sustainable development, specifically focusing on One Belt One Road (OBOR) countries. Using OBOR countries, panel data is collected over 24 years from 1996 to 2019.An enhanced System GMM estimator is employed in order to overcome challenges associated with methodology such as endogeneity, homoscedasticity and serial correlation which ensures reliable and robust payoff results.
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