IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 3 2025
DOI: 10.56201/ijcsmt.vol.11.no3.2025.pg20.37

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An Enhanced Framework for Monitoring Toddlers’ Safety in Smart Homes Using Deep Learning

Onyinyechi Echezona Nwosu, Nuka Nwaibu, Dum Sako, , Mask RCNN

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Abstract

Monitoring toddlers in smart homes is essential for ensuring their safety due to their unpredictable behavior and the varying nature of home environments. This dissertation presents a novel toddler detection and tracking system that surpasses existing methods by employing an optimized Convolutional Neural Network (CNN). The developed system is capable of detecting and tracking multiple toddlers simultaneously, offering a scalable architecture that can easily integrate future Mask R-CNN advancements. Implemented using Python, TensorFlow, and Keras, and structured with the Object-Oriented Analysis and Design (OOAD) methodology, the system achieves a remarkable accuracy of 98.94%. This significant improvement demonstrates its superior robustness and effectiveness in handling dynamic and diverse home environments. The enhancements made include the fine-tuning of CNN parameters to better accommodate various lighting conditions and occlusions commonly found in home settings. Real-time preprocessing ensures that the system maintains high accuracy without compromising processing speed, making it suitable for continuous monitoring applications. Transfer learning with MobileNetV2 allows the system to leverage pre-trained models, accelerating the training process and improving generalization across different home layouts. The system's high accuracy of 98.94% not only highlights its effectiveness but also positions it as a reliable solution for enhancing toddler safety in smart homes.

keywords:

Toddler monitoring system, Mask r-cnn, Harmful objects, Computer vision.

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References:

Arshad, B., Ogie, R., Barthélemy, J., Pradhan, B., Verstaevel, N., & Perez, P. (2019). Computer vision
and iot-based sensors in flood monitoring and mapping: a systematic review. Sensors, 19(22), 5012.
Balakrishnan, S., Kumar, K., Lakshmanan, L., & Muthusundar, S. (2021). Iot for health monitoring system
based on machine learning algorithm. Wireless Personal Communications, 124(1), 189-205.
Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., & Chen, X. (2021). Deep learning methods for heart sounds
classification: a systematic review. Entropy, 23(6), 667.
Correia, L., Fuentes, D., Ribeiro, J., Costa, N., Reis, A., Rabadão, C., … & Pereira, A. (2021). Usability
of smartbands by the elderly population in the context of ambient assisted living applications.
Electronics, 10(14), 1617.
Diao, Z., & Sun, F. (2022). Visual Object Tracking Based on Deep Neural Network. Mathematical
Problems in Engineering, 2022.
Forkan, A., Branch, P., Jayaraman, P., & Ferretto, A. (2019). Halleyassist: a personalised internet of things
technology to assist the elderly in daily living.. https://doi.org/10.24251/hicss.2019.507
Hong, N., Kim, M., Jun, M., & Kang, J. (2017). A study on a jwt-based user authentication and api
assessment scheme using imei in a smart home environment. Sustainability, 9(7), 1099.
Kalake, L., Wan, W., & Hou, L. (2021). Analysis based on recent deep learning approaches applied in
real-time multi-object tracking: a review. IEEE Access, 9, 32650-32671.
Kang, H., Han, J., & Kwon, G. (2021). Determining the intellectual structure and academic trends of smart
home health care research: coword and topic analyses. Journal of Medical Internet Research, 23(1),
e19625.
Khan, M., Silva, B., & Han, K. (2016). Internet of things based energy aware smart home control system.
Ieee Access, 4, 7556-7566.
Kumar, P., Braeken, A., Gurtov, A., Iinatti, J., & Ha, P. (2017). Anonymous secure framework in
connected smart home environments. Ieee Transactions on Information Forensics and Security,
12(4), 968-979.
Kumar, P., Gurtov, A., Iinatti, J., Ylianttila, M., & Sain, M. (2016). Lightweight and secure session-key
establishment scheme in smart home environments. Ieee Sensors Journal, 16(1), 254-264.
Lee, D. D., & Seung, H. (2021). A neural network based head tracking system. Advances in Neural
Information Processing Systems, 10.
Li, W., Yi?itcanlar, T., Liu, A., & Erol, I. (2022). Mapping two decades of smart home research: a
systematic scientometric analysis. Technological Forecasting and Social Change, 179, 121676.
Meimetis, D., Daramouskas, I., Perikos, I., & Hatzilygeroudis, I. (2023). Real-time multiple object
tracking using deep learning methods. Neural Computing and Applications, 35(1), 89-118.
Nayak, R., Behera, M. M., Girish, V., Pati, U. C., & Das, S. K. (2019, December). Deep learning based
loitering detection system using multi-camera video surveillance network. In 2019 IEEE
International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 215-220). IEEE.
Nguyen, L. A., Dung, P. T., Khoa, T. D., Son, N. H., Hiep, N. T., Van Nguyen, P., ... & Truong, X. T.
(2018, November). Deep learning-based multiple objects detection and tracking system for socially
aware mobile robot navigation framework. In 2018 5th NAFOSTED Conference on Information and
Computer Science (NICS) (pp. 436-441). IEEE.
NST, D. (2023). Development of ar android application for disability and elderly-friendly smart home
control. International Journal of Information Technology and Computer Engineering, (41), 32-42.
Parmar, Y., Natarajan, S., & Sobha, G. (2019). Deeprange: deep?learning?based object detection and
ranging in autonomous driving. IET Intelligent Transport Systems, 13(8), 1256-1264.
Phan, L. and Kim, T. (2020). Breaking down the compatibility problem in smart homes: a dynamically
updatable gateway platform. Sensors, 20(10), 2783.
Ravì, D., Wong, C., Lo, B., & Yang, G. (2016). Deep learning for human activity recognition: a resource
efficient implementation on low-power devices.. https://doi.org/10.1109/bsn.2016.7516235
Sánchez, V., Taylor, I., & Bing?Jonsson, P. (2017). Ethics of smart house welfare technology for older
adults: a systematic literature review. International Journal of Technology Assessment in Health
Care, 33(6), 691-699.
Sanchez-Comas, A., Synnes, K., & Hallberg, J. (2020). Hardware for recognition of human activities: a
review of smart home and aal related technologies. Sensors, 20(15), 4227.
Shouran, Z., Ashari, A., & Priyambodo, T. (2019). Internet of things (iot) of smart home: privacy and
security. International Journal of Computer Applications, 182(39), 3-8.
Unlu, E., Zenou, E., Riviere, N., & Dupouy, P. E. (2019). Deep learning-based strategies for the detection
and tracking of drones using several cameras. IPSJ Transactions on Computer Vision and
Applications, 11(1), 1-13.
Wang, E. K., Chen, C. M., Hassan, M. M., & Almogren, A. (2020). A deep learning based medical image
segmentation technique in Internet-of-Medical-Things domain. Future Generation Computer
Systems, 108, 135-144.
Wang, P., Ye, F., & Chen, X. (2018). A smart home gateway platform for data collection and awareness.
Ieee Communications Magazine, 56(9), 87-93.
Xue, H. (2023). Study on the development status of smart home: huawei smart home as an example.
Advances in Economics Management and Political Sciences, 21(1), 40-44.
Yang, T., Zhang, G., Li, Y., Yang, Y., Wang, H., & Zhang, Y. (2022). Detecting privacy leakage of smart
home devices through traffic analysis. Security and Communication Networks, 2022, 1-10.

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