Cover
Vol. 2 No. 1 (2026)

Published: June 1, 2026

Pages: 76-87

Review Article

A Narrative Review of AI and IoT-Based Systems for Child Fall Detection and Health Monitoring

Fatin Abdalwahab 1 Ruba Ibrahim 1
1 Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq
History
  • Received: January 7, 2026
  • Revised: February 3, 2026
  • Accepted: March 12, 2026
  • Available Online: June 1, 2026
DOI

https://doi.org/10.33971/ijmrai.2.1.8

Abstract

This narrative study provides an analytical and critical review of recent advancements (2019-2026) in the integration of IoT (Internet of Things) and AI (Artificial Intelligence) systems for fall detection and child health monitoring. Unlike prior studies, which concentrated on elderly care and monitoring, this study examines child-specific monitoring environments, including wearable, vision-based, and hybrid systems. It investigates new trends such as the combination of deep learning and interpretable AI with multimedia sensory input and peripheral or fuzzy computing. Data scarcity, real-world deployment limits, privacy concerns, and age-related changes are among the key challenges addressed. The paper identifies important research gaps and proposes future paths for sustainable, secure, and accessible intelligent child monitoring systems.

Keywords

References

  1. T. Shelke and P. Kolhe, “A Survey Review of IoT Based Baby Monitoring System Using Raspberry PI,” International Journal of Research Publication and Reviews, vol. 3, no. 6, pp. 1537–1541, Jun. 2022.
  2. S. Saeedbakhsh, M. Mohammadi, S. Younesi, and M. Sattari, “Using Internet of Things for child care: A systematic review,” International Journal of Preventive Medicine, vol. 16, p. 3, Jan. 2025. https://doi.org/10.4103/ijpvm.ijpvm_191_23
  3. J. Zhang, Z. Li, Y. Liu, J. Li, H. Qiu, M. Li, G. Hou, and Z. Zhou, “An effective deep learning framework for fall detection: Model development and study design,” J. Med. Internet Res., vol. 26, p. e56750, Aug. 2024. https://doi.org/10.2196/56750
  4. S. H. Grandhi, “Enhancing Children’s Health Monitoring: Adaptive Wavelet Transform in Wearable Sensor IoT Integration,” Journal of Current Science & Humanities, vol. 10, no. 4, pp. 15–27, 2022.
  5. M. Arbaoui, M.-E.-A. Brahmia, and A. Rahmoun, “A review of IoT architectures in smart healthcare applications,” in Proceedings of the 7th International Conference on Fog and Mobile Edge Computing (FMEC), Paris, France, Dec. 2022, pp. 1–8, 2022. https://doi.org/10.1109/FMEC57183.2022.10062841
  6. M. Raeisi Varzaneh et al., “Internet of things: Security, issues, threats, and assessment of different cryptographic technologies,” Journal of Communications, vol. 19, no. 2, pp. 78–89, 2024. https://doi.org/10.12720/jcm.19.2.78-89
  7. H. M. N. Hasan, S. Islam, A. H. Hussain, M. M. Islam, and M. M. Hassan, “Personalized Health Monitoring of Autistic Children Through AI and IoT Integration,” Journal of International Crisis and Risk Communication Research, vol. 7, no. S4, pp. 358–365, Apr. 2024. https://doi.org/10.63278/jicrcr.vi.3315
  8. Z. Xu, H. Zhou, S. Peng, et al., “Towards Depth Foundation Models: Recent Trends in Vision-Based Depth Estimation,” Computational Visual Media, preprint, 2025. https://doi.org/10.48550/arXiv.2507.11540
  9. S. P. Mishra, A. Kamaraj, V. Rajinikanth, and M R. Rahul, “A computer vision-based approach for identification of non-metallic inclusions in the steel industry products,” Journal of Industrial Information Integration, vol. 46, p. 100860, 2025. https://doi.org/10.1016/j.jii.2025.100860
  10. E. Mustafa, J. Shuja, S. K. uz Zaman, A. I. Jehangiri, S. Din, F. Rehman, S. Mustafa, T. Maqsood, and A. N. Khan, “Joint wireless power transfer and task offloading in mobile edge computing: a survey,” Cluster Computing, vol. 25, no. 4, pp. 2429– 2448, 2022. https://doi.org/10.1007/s10586-021-03376-3
  11. M. Maray, E. Mustafa, and J. Shuja, “Wireless Power Assisted Computation Offloading in Mobile Edge Computing: A Deep Reinforcement Learning Approach,” Human-Centric Computing and Information Sciences, vol. 14, art. no. 22, pp. 19–39, 2024. https://doi.org/10.22967/HCIS.2024.14.022
  12. X. Hou, Y. Hu, and F. Wang, “Overview of task offloading of wireless sensor network in edge computing environment,” in Proc. 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT), Qingdao, China, Jul. 2023, pp. 1018–1022. https://doi.org/10.1109/ICEICT57916.2023.10245473
  13. K. Zhang, J. Ni, K. Yang, X. Liang, J. Ren, and X. S. Shen, “Security and privacy in smart city applications: Challenges and solutions,” IEEE Communications Magazine, vol. 55, no. 1, pp. 122–129, Jan. 2017. https://doi.org/10.1109/MCOM.2017.1600267CM
  14. S. Zhang, “AI-assisted early screening, diagnosis, and intervention for autism in young children,” Frontiers in Psychiatry, vol. 16, Apr. 2025. https://doi.org/10.3389/fpsyt.2025.1513809
  15. Amendolara, A. B., Sant, D., Rotstein, H. G., & Fortune, “LSTM-based recurrent neural network provides effective short term flu forecasting. BMC Public Health,” vol. 23, no.1788, 2023. https://doi.org/10.1186/s12889-023-16720-6
  16. J. Lin, Y. Tan, Y. Lin, and X. Qiu, “Artificial intelligence-enhanced diagnostics for pediatric asthma and respiratory irregularities using deep learning and wearable sensors,” PeerJ Computer Science, vol. 12, e3382, Jan. 2026, doi: 10.7717/peerj-cs.3382.
  17. D. Ravi, C. Wong, B. Lo, and G.-Z. Yang, “A deep learning approach to on-node sensor data analytics for mobile or wearable devices,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 56–64, Jan. 2017. https://doi.org/10.1109/jbhi.2016.2633287
  18. H. Ahmed, G. Al-Suhail, and A. Abood, “Next-Generation of Smart Healthcare: A Review of Emerging AI Technologies and Their Clinical Applications”, International Journal of Mechatronics, Robotics, and Artificial Intelligence, vol.1, issue 2, pp. 94-103, 2025. https://doi.org/10.33971/ijmrai.1.2.12
  19. I. Trabelsi, J. Françoise, and Y. Bellik, “Sensor-based Activity Recognition using Deep Learning: A Comparative Study,” in Proceedings of the 8th International Conference on Movement and Computing (MOCO ’22), Chicago, IL, USA, no. 20, pp. 1–8, June 30 Jun., 2022. https://doi.org/10.1145/3537972.3537996
  20. I. K. Ihianle, A. O. Nwajana, S. H. Ebenuwa, R. I. Otuka, K. Owa, and M. O. Orisatoki, “A Deep Learning Approach for Human Activities Recognition from Multimodal Sensing Devices,” IEEE Access, vol. 8, pp. 179028–179037, 2020. https://doi.org/10.1109/ACCESS.2020.3027979
  21. A. M. Ahmadi, K. Kiani, and R. Rastgoo, “A Transformer-Based Model for Abnormal Activity Recognition in Video,” Journal of Modeling in Engineering, vol. 22, no. 76, pp. 213–221, 2024. https://doi.org/10.22075/jme.2024.32914.2604
  22. C. Cappa, N. Petrowski, A. Deliege, and M. R. Khan, “Monitoring the situation of children living in residential care: Data gaps and innovations,” Vulnerable Children and Youth Studies, vol. 17, no. 2, pp. 110–118, 2022. https://doi.org/10.1080/17450128.2021.1996669
  23. S. Zakerabasali and S. M. Ayyoubzadeh, “Internet of Things and healthcare system: A systematic review of ethical issues,” Health Science Reports, vol. 5, no. 6, p. e863, 2022. https://doi.org/10.1002/hsr2.863
  24. S. Subramaniam, A. I. Faisal, and M. J. Deen, “Wearable sensor systems for fall risk assessment: A review,” Frontiers in Digital Health, vol. 4, Jul., 2022. https://doi.org/10.3389/fdgth.2022.921506
  25. A. Ramachandran and A. Karuppiah, “A survey on recent advances in wearable fall detection systems,” BioMed Research International, vol. 2020, no. 1, p. 2167160, 2020. https://doi.org/10.1155/2020/2167160
  26. N. K. M. Yusof, M. Z. M. Zabidi, and N. A. Hamzah, “Development of a child fall detection system based on YOLOv9 using transfer learning from adult fall datasets,” Evolution in Electrical and Electronic Engineering, vol. 6, no. 2, pp. 45–56, 2025.
  27. Z. Yang, B. Lsui, J. Ning and Z. Wu, “Falling detection of toddlers based on improved YOLOv8 models,” Sensors, vol. 24, no 19, Art. no. pp. 64-51, 2024. https://doi.org/10.3390/s24196451
  28. M. Chen, H. Wang, L. Yu, E. H. K. Yeung, J. Luo, K.-L. Tsui, and Y. Zhao, “A systematic review of wearable sensor-based technologies for fall risk assessment in older adults,” Sensors, vol. 22, no. 18, pp. 67-52, 2022, https://doi.org/10.3390/s22186752
  29. G. L. Santos, P. T. Endo, K. H. C. Monteiro, E. S. Rocha, I. Silva, and T. Lynn, ""Accelerometer-Based Human Fal Detection Using Convolutional Neural Networks,"" Sensors, vol. 19, no. 7, p. 1644, Apr. 2019, https://doi.org/10.3390/s19071644
  30. D. Mrozek, A. Koczur and B. Małysiak-Mrozek, ""Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge,"" Information Sciences, vol. 537, pp. 132–147, May 2020. https://doi.org/10.1016/j.ins.2020.05.070
  31. A. A. Kadhum, H. Al-Libawy, and E. A. Hussein, ""An Accurate Fall Detection System for the Elderly People Using Smartphone Inertial Sensors,"" Journal of Physics: Conference Series, vol. 1530, p. 012102, 2020, doi: 10.1088/1742- 6596/1530/1/012102.
  32. T. Vaiyapuri, E. L. Lydia, M. Y. Sikkandar, V. G. Díaz, I. V. Pustokhina, and D. A. Pustokhin, "Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare," IEEE Access, vol. 9, pp. 113879–113888, 2021. https://doi.org/10.1109/ACCESS.2021.3094243
  33. N. Zurbuchen, A. Wilde, and P. Bruegger, ""A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection,"" Sensors, vol. 21, no. 3, p. 938, Jan. 2021. https://doi.org/10.3390/s21030938
  34. S. Vimal, Y. H. Robinson, S. Kadry, H. V. Long, and Y. Nam, ""IoT Based Smart Health Monitoring with CNN Using Edge Computing,"" Journal of Internet Technology, vol. 22, no. 1, pp. 173-185, Jan. 2021.
  35. A. Sowmya and A. S. Pillai, “Human Fall Detection with Wearable Sensors Using Machine Learning Algorithms,” Proceedings of the 2nd International Conference on Smart Electronics and Communication (ICOSEC), IEEE, pp. 1092–1095, 2021. https://doi.org/10.1109/ICOSEC51865.2021.9591912
  36. R. S. Raghavendrachar, S. Nayak, D. Vishnupriya, R. A. Rahman, and K. K. N. Krithika, “Wearable Safety Device for Children,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 10, no. 4, pp. 1377–1381, Apr. 2022. https://doi.org/10.22214/ijraset.2022.41432
  37. F. Castro, V. Dentamaro, V. Gattulli, and D. Impedovo, “Fall Detection with LSTM and Attention Mechanism,” in Proc. Workshop on Advances of Mobile and Wearable Biometrics (WAMWB’23), Athens, Greece, Sep. 26, 2023, CEUR Workshop Proceedings.
  38. H. A. Alharbi, K. K. Alharbi, and C. A. U. Hassan, “Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability,” Sustainability, vol. 15, no. 22, p. 15695, 2023. https://doi.org/10.3390/su152215695
  39. S. L. Sahithi, V. Anuradha, J. S. Vinod, C. U. Kumar, P. Swarnalatha, and R. V. V. Krishna, “IoT-Based Baby Monitoring System,” Int. J. Sci. Res. Eng. Manag. (IJSREM), vol. 7, no. 4, Apr. 2023.
  40. H. Alam, M. Burhan, A. Gillani, et al, “IoT Based Smart Baby Monitoring System with Emotion Recognition Using Machine Learning,” Wireless Communications and Mobile Computing, vol. 2023, no. 1, pp. 1–13, 2023. https://doi.org/10.1155/2023/1175450
  41. S. K. Sona and S. Swapna Kumar, “IoT-Based System for Real-Time Fall Risk Assessment and Health Monitoring,” Journal of Electronics and Electrical Engineering, vol. 3, no. 2, 2024. https://doi.org/10.37256/jeee.3220245472.
  42. M. C. Sanga, N. P. Fosah, and G. W. Ejuh, ""Design and realization of an autonomous multi-powered IoT-based infant incubator monitoring system hardware module,"" Int. J. Res. Adv. Electron. Eng., vol. 6, no. 1, pp. 56–65, 2025. https://doi.org/10.22271/27084558.2025.v6.i1a.53
  43. S. Thangam, M. Gurupriya, A. A. Vardhan, M. A. U. Sai, and Y. M. P. Kumar, “Smart Health Monitoring System Using Io and Machine Learning,” in Challenges in Information, Communication and Computing Technology, V. Sharmila et al., Eds., London, U.K.: Taylor & Francis, 2025, pp. 518–523. https://doi.org/10.1201/9781003559085-90
  44. K. Chandnani, S. Tripathy, A. K. Parbhakar, K. Takiar, U. Singhal, P. Sasikumar, and S. Maheswari, “A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care,” Scientific Reports, vol. 15, no. 19080, May 2025, https://doi.org/10.1038/s41598-025-02691-8
  45. Y. Sanjalawe, S. Fraihat, M. Abualhaj, S. R. Al-E’mari, and E. Alzubi, “Hybrid deep learning for human fall detection: A synergistic approach using YOLOv8 and time-space transformers,” IEEE Access, vol. 13, pp. 41336–41352, Mar. 2025. https://doi.org/10.1109/ACCESS.2025.3547914
  46. M. Lupión, V. González-Ruiz, J. F. Sanjuan, and P. M. Ortigosa, “Privacy-aware fall detection and alert management in smart environments using multimodal devices,” Internet of Things, vol. 30, 101526, 2025. https://doi.org/10.1016/j.iot.2025.101526
  47. X. Huang, X. Li, L. Yuan, Z. Jiang, H. Jin, W. Wu, R. Cai, M. Zheng, and H. Bai, “SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments “, Scientific Reports, vol. 15, no. 1, pp. 1–18, 2025, https://doi.org/10.1038/s41598-025-86593-9
  48. N. K. M. Yusof, S. Sari, N. S. A. M. Taujuddin, H. Roslan, and N. Ibrahim, “Development of a Child Fall Detection System based on YOLOv9 using Transfer Learning from adult Fall Datasets,” Evolution in Electrical and Electronic Engineering, vol. 6, no. 2, pp. 70–82, Oct. 2025. https://doi:10.30880/eeee.2025.06.02.009.
  49. Y. Sanjalawe, S. Fraihat, M. Abualhaj, S. R. Al-E’mari, and E. Alzubi, “Hybrid Deep Learning for Human Fall Detection: A Synergistic Approach Using YOLOv8 and Time-Space Transformers,” IEEE Access, vol. 13, pp. 41336–41351, 2025. https://doi.org/10.1109/ACCESS.2025.3547914.
  50. R. S. Shankar, T. Shailesh, et al., “Smart health monitoring: an interactive Raspberry Pi-based system for managing diabetes and hypertension in children,” Journal of Cloud Computing, vol. 14, p. 70, 2025. https://doi.org/10.1186/s13677-025-00796-z
  51. L. Hu, H. Meng, Z. Xu, and Y. Wang “A self-powered wearable sensor for infant fall detection based on triboelectric nanogenerator,” Applied Physics A, vol. 131, no. 3, p. 164, 2025. https://doi.org/10.1007/s00339-025-08305-4
  52. A. Giri, A. Hasib, M. Islam, M. F. Tazim, M. S. Rahman, and A. Akib, “Real-time human fall detection using YOLOv5 on Raspberry Pi: An edge AI solution for smart healthcare and safety monitoring,” in Proc. International Conference on Data Analytics & Management, Cham, Switzerland: Springer Nature Switzerland, pp. 493–507, Jun. 2025. https://doi.org/10.1007/978-3-032-02831-0_40
  53. N. R. Wagh, S. R. Sutar, V. J. Kadam, S. M. Jadhav, A. S. Yadav, and V. S. Pawar, “Evaluation of IoT based smart safety systems for women and children using machine learning techniques,” Scientific Reports, vol. 16, Art. no. 87, 2026. https://doi.org/10.1038/s41598-025-29146-4