Smart Cradle: A Technology-Enabled Solution for Safer and Better Infant Sleep
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Abstract
The Smart Cradle using Internet of Things (IoT) is a novel and innovative approach to modernize traditional cradle systems by incorporating smart and connected technologies. This IoT-based cradle system offers enhanced safety, comfort, and convenience for both babies and caregivers. The Smart Cradle is equipped with various sensors such as temperature, humidity, motion, and sound sensors that continuously monitor the baby's environment. These sensors collect data in real-time and send it to a cloud-based server for processing and analysis. The caregivers can access this data through a mobile application or a web interface, allowing them to remotely monitor the baby's condition and receive alerts in case of any abnormalities. Furthermore, the Smart Cradle incorporates features like automated rocking, adjustable incline, and soothing lullabies, which can be controlled remotely through the mobile application. The caregivers can customize the cradle's settings based on the baby's preferences and needs, providing a personalized sleeping experience for the baby. Additionally, the Smart Cradle offers seamless integration with other smart home devices, such as smart cameras, smart lights, and smart thermostats, enabling caregivers to create a safe and conducive environment for the baby. The system can also generate insights and recommendations based on the collected data, helping caregivers to make informed decisions about the baby's sleep patterns, health, and well- being.
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S. Kavitha, R. R. Neela, M. Sowndarya, Madhuchandra and K. Harshitha, "Analysis on IoT Based Smart Cradle System with an Android Application for Baby Monitoring," 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), 2019, pp. 136-139, doi:10.1109/ICATIECE45860.2019.9063773.
W. A. Jabbar, H. K. Shang, S. N. I. S. Hamid, A. A. Almohammedi, R. M. Ramli and M. A. H. Ali, "IoT- BBMS: Internet of Things-Based Baby Monitoring System for Smart Cradle," in IEEE Access, vol. 7, pp. 93791- 93805, 2019, doi: 10.1109/ACCESS.2019.2928481.
Mamodiya and D. N. Tiwari, “The performance enhancing technique analysis for automatic tracking tilt angle optimization of the solar panel with soft- computing process,” International Journal of Advance Science and Technology, vol. 29, 10Supplement, pp. 3587–3601, 2020.
O. Taiwo and A. E. Ezugwu, “Internet of Things- based intelligent smart home control system,” Security and Communication Networks, vol. 2021, Article ID 9928254, 17 pages, 2021.
Tanaka, A., Min-ji, K., Silva, C., Cohen, D., & Mwangi, J. Predictive Analytics for Healthcare Resource Allocation. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/150
Rachna Palaskar, Shweta Pandey, Ashwini Telang, Akshada Wagh, Ramesh R. Kagalkar, “An Automatic Monitoring and Swing the Baby Cradle for Infant Care” Int. J. of Advanced Research in Computer and Commun. Eng., Dec 2015.
S. Durga, S. Itnal, K. Soujanya, C. Z. Basha and C. Saxena, "Advanced and effective baby care monitoring Smart cradle system using Internet of Things," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 35-42, doi: 10.1109/ICOSEC51865.2021.9591955.
V. Kumar, D. Kumar, M. Kaur, D. Singh, S. A. Idris, and H. Alshazly, “A novel binary seagull optimizer and its application to feature selection problem,” IEEE Access, vol. 9, pp. 103481–103496, 2021.
C, S. L. ., Saxena, S. ., & Kumar, B. S. . (2023). Design Text Mining Classifier for Covid-19 by using the Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 240 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2622
W. A. Jabbar, H. K. Shang, S. N. I. S. Hamid, A. A. Almohammedi, R. M. Ramli, and M. A. H. Ali, “IoT- BBMS: Internet of Things-based baby monitoring system for smart cradle,” IEEE Access, vol. 7, pp. 93791–93805, 2019.
N. M. Holekamp, J. Campbell, A. Almony et al., “Vision outcomes following anti-vascular endothelial growth factor treatment of diabetic macular edema in clinical practice,” American Journal of Ophthalmology, vol. 191, pp. 83–91, 2018.
M. Görs, M. Albert, K. Schwedhelm, C. Herrmann, and K. Schilling, “Design of an advanced telemedicine system for remote supervision,” IEEE Systems Journal, vol. 10, no. 3, pp. 1089–1097, 2015.
Mr. A. Kingsly Jabakumar. (2019). Enhanced QoS and QoE Support through Energy Efficient Handover Algorithm for UMTS Architectures. International Journal of New Practices in Management and Engineering, 8(01), 01 - 07. https://doi.org/10.17762/ijnpme.v8i01.73
(M. Sathya, M. Jeyaselvi, L. Krishnasamy et al., “A novel, efficient, and secure anomaly detection technique using DWU-ODBN for IoT-enabled multimedia communication systems,” Wireless Communications and Mobile Computing, vol. 2021, Article ID 4989410, 12 pages, 2021.
B. B. Francis, A. Robert LeBlanc, J. B. Nasmith, D. Palisaitis, B. Dubé, and R. Nadeau, “Continuous ST-segment monitoring during coronary angioplasty using orthogonal ECG leads,” Journal of Electrocardiology, vol. 30, no. 3, pp. 175–187, 1997.
M. H. McGillion, E. Duceppe, K. Allan et al., “Postoperative remote automated monitoring: need for and state of the science,” Canadian Journal of Cardiology, vol. 34, no. 7, pp. 850–862, 2018.
X. Liu, K. Takeuchi, T. Ogunfunmi and S. Mathapathi, "Video-based IoT baby monitor for SIDS prevention," 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, USA, 2017, pp. 1-7, doi: 10.1109/GHTC.2017.8239258.
Fatima Abbas, Deep Learning Approaches for Medical Image Analysis and Diagnosis , Machine Learning Applications Conference Proceedings, Vol 3 2023.
“Sudden Unexpected Infant Death and Sudden Infant Death Syndrome”, Centers for Disease Control and Prevention. Centers for Disease Control and Prevention 17 Apr. 2017, May 2017