AI-StuntDetect: Development of Early Stunting Detection System for Young Children Based on CNN and MediaPipe Pose
Abstract
Stunting remains one of Indonesia's most persistent public health problems. Yet, growth monitoring at community health posts (posyandu) still relies on manual anthropometric measurements, which are time-consuming, operator-dependent, and prone to recording errors. This study aims to develop and evaluate a low-cost, image-based system to estimate toddlers' height and weight from a single photograph, enabling earlier stunting screening at the community level. Its main contribution lies not only in the system itself but in the evaluation approach. Instead of reporting error metrics in isolation, both models are benchmarked against a mean-value baseline, exposing a misleading interpretation that commonly arises when absolute error metrics are compared across variables with different units and ranges. The system combines a Convolutional Neural Network with MediaPipe Pose landmark detection, and was tested on 13 toddlers by comparing predicted values against manual measurements using MAE, MSE, and RMSE. The height model achieved an MAE of 4.886 cm, MSE of 37.839 cm², and RMSE of 6.151 cm, outperforming the baseline by 63.9%, 82.6%, and 58.3%, respectively. The weight model recorded a seemingly smaller MAE of 2.630 kg, MSE of 9.693 kg², and RMSE of 3.113 kg, yet improved on the baseline by only 0.6% in MAE and performed worse in MSE and RMSE, indicating that the low error reflects the narrow weight range of the test sample (6.60–14.50 kg) rather than genuine predictive capability. Average processing time was 1.185 seconds per sample. These findings show that a single 2D image is sufficient for estimating height, a linear measure, but inadequate for weight, which depends on body volume and depth information. The system is therefore recommended for preliminary height screening, while weight measurement still requires conventional scales until multi-angle or depth-based imaging is incorporated.
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References
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[2] P. K. Arya, K. Sur, T. Kundu, S. Dhote, and S. K. Singh, “Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data,” Nutrition, vol. 132, p. 112674, 2025, doi: https://doi.org/10.1016/j.nut.2024.112674.
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[7] I. Mostafa et al., “A microbiota-directed complementary food intervention in 12–18-month-old Bangladeshi children improves linear growth,” EBioMedicine, vol. 104, p. 105166, 2024, doi: https://doi.org/10.1016/j.ebiom.2024.105166.
[8] Md. M. Islam et al., “Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh,” International Journal of Cognitive Computing in Engineering, vol. 3, pp. 46–57, 2022, doi: https://doi.org/10.1016/j.ijcce.2022.02.002.
[9] R. E. Kofinti, I. Koomson, J. A. Paintsil, and E. K. Ameyaw, “Reducing children’s malnutrition by increasing mothers’ health insurance coverage: A focus on stunting and underweight across 32 sub-Saharan African countries,” Econ. Model., vol. 117, p. 106049, 2022, doi: https://doi.org/10.1016/j.econmod.2022.106049.
[10] E. Gayawan and O. A. Egbon, “Spatio-temporal mapping of stunting and wasting in Nigerian children: A bivariate mixture modeling,” Spat. Stat., vol. 58, p. 100785, 2023, doi: https://doi.org/10.1016/j.spasta.2023.100785.
[11] S. V Subramanian, O. Karlsson, and R. Kim, “Revisiting the stunting metric for monitoring and evaluating nutrition policies,” Lancet Glob. Health, vol. 10, no. 2, pp. e179–e180, 2022, doi: https://doi.org/10.1016/S2214-109X(21)00504-0.
[12] L. Bevis, K. Kim, and D. Guerena, “Soil zinc deficiency and child stunting: Evidence from Nepal,” J. Health Econ., vol. 87, p. 102691, 2023, doi: https://doi.org/10.1016/j.jhealeco.2022.102691.
[13] B. Haile and D. Headey, “Growth in milk consumption and reductions in child stunting: Historical evidence from cross-country panel data,” Food Policy, vol. 118, p. 102485, 2023, doi: https://doi.org/10.1016/j.foodpol.2023.102485.
[14] UNICEF / WHO / World Bank Group Joint Child Malnutrition Estimates, “Levels and trends in child malnutrition,” who.int/publications, no. 978-92-4-007379–1, pp. 1–32, 2023.
[15] R. D. Logarajan, N. M. Nor, S. Ibrahim, and R. Said, “Social determinants of stunting in Malay children aged <5 years in Malaysia,” Nutrition, vol. 111, p. 112030, 2023, doi: https://doi.org/10.1016/j.nut.2023.112030.
[16] M. B. Arndt et al., “Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021,” The Lancet, vol. 404, no. 10471, pp. 2543–2583, 2024, doi: https://doi.org/10.1016/S0140-6736(24)01821-X.
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[18] N. Khalid, R. Nawaz, Z. Zhou, and S. Gong, “Child malnutrition in Pakistan: pathways linking maternal intimate partner violence, gender inequality, healthcare utilization, and age at marriage,” Child. Youth Serv. Rev., vol. 186, p. 109039, 2026, doi: https://doi.org/10.1016/j.childyouth.2026.109039.
[19] K. Simamora, B. W. Otok, and S. Setiawan, “Multilevel Confirmatory Factor Analysis for Modelling the Determinants of Malnutrition Among Under-Five Children in Indonesia,” in 2025 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/ICoABCD67551.2025.11470742.
[20] R. Mukherjee et al., “Accuracy of a smartphone-based 3D imaging tool for height measurement and stunting detection among children 24–59 months of age in Nepal: a validation study,” The Lancet Regional Health - Southeast Asia, vol. 48, p. 100774, 2026, doi: https://doi.org/10.1016/j.lansea.2026.100774.
[21] Muh. Saleh, A. Yudianti, D. S. Damayati, S. Basri, and M. A. Munawir Amansyah, “Evaluasi Peran Kepemilikan Jamban dalam Kejadian Stunting: Temuan dari Kabupaten Jeneponto, Sulawesi Selatan,” Jurnal Kesehatan Lingkungan Indonesia, vol. 24, no. 1, pp. 101–108, Feb. 2025, doi: 10.14710/jkli.24.1.101-108.
[22] Ministry Of Health Of The Republic Of Indonesia Health Development Policy Agency, Indonesian Health Survei 2023 In Numbers. Ministry Of Health Of The Republic Of Indonesia, 2024.
[23] S. Ichikawa, M. Hamada, and H. Sugimori, “A deep-learning method using computed tomography scout images for estimating patient body weight,” Sci. Rep., vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-95170-9.
[24] A. Demircioğlu, A. S. Quinsten, L. Umutlu, M. Forsting, K. Nassenstein, and D. Bos, “Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning,” Sci. Rep., vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-46080-5.
[25] L. D. B. Lima et al., “A scale-equivariant CNN-based method for estimating human weight and height from multi-view clinic silhouette images,” Expert Syst. Appl., vol. 256, Dec. 2024, doi: 10.1016/j.eswa.2024.124879.
[26] V. G. Sonar et al., “3D Camera-Based Body Weight Estimation Using Artificial Intelligence in Emergency Care Settings,” Emergency Care and Medicine, vol. 2, no. 4, p. 55, Nov. 2025, doi: 10.3390/ecm2040055.
[27] H. Shu, L. Ren, L. Pan, D. Huang, H. Lu, and W. Wang, “Single Image based Infant Body Height and Weight Estimation,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE Computer Society, 2023, pp. 6052–6059. doi: 10.1109/CVPRW59228.2023.00644.
[28] M. Sakina, I. Muhammad, and S. S. Abdullahi, “A multi-factor approach for height estimation of an individual using 2D image,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 765–770. doi: 10.1016/j.procs.2023.12.140.
[29] Risfendra, Aripriharta, Suherman, G. F. Ananda, D. S. Putra, and Fahmi, “Contactless Infant Height Measurement for Enhanced Early Detection of Stunting Using Computer Vision Techniques,” IEEE Access, vol. 13, pp. 42364–42376, 2025, doi: 10.1109/ACCESS.2025.3548159.
[30] L. Qiao et al., “Contactless Height Measurement for Infants and Toddlers Based on Computer Vision,” in 2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE), 2024, pp. 306–310. doi: 10.1109/ICCECE61317.2024.10504244.
[31] H. and T. D.-T. and S. V. K. Kien Nguyen Phan and Van Thao, “Height Measurement of Pose Bent Knees by Using Pose Estimation of MediaPipe,” in Advances in Data and Information Sciences, M. C. and K. M. L. and S. B. K. Tiwari Shailesh and Trivedi, Ed., Singapore: Springer Nature Singapore, 2025, pp. 357–365. doi: https://doi.org/10.1007/978-981-97-9619-9_29
[32] P. Nguyen Trung et al., “A Non-Contacted Height Measurement Method in Two-Dimensional Space,” Sensors, vol. 24, no. 21, Nov. 2024, doi: 10.3390/s24216796.
[33] R. E. Caraka et al., “Bayesian Network analysis of spatial disparities in child nutrition status and health determinants across Indonesia,” International Journal of Applied Earth Observation and Geoinformation, vol. 144, p. 104936, 2025, doi: https://doi.org/10.1016/j.jag.2025.104936.
[34] A. Sánchez, M. Favara, M. Sheridan, and J. Behrman, “Does early nutrition predict cognitive skills during later childhood? Evidence from two developing countries,” World Dev., vol. 176, p. 106480, 2024, doi: https://doi.org/10.1016/j.worlddev.2023.106480.
[35] W. R. Mgomezulu, P. Thangata, B. Mkandawire, and N. Amoah, “Advancing predictive analytics in child malnutrition: Machine, ensemble and deep learning models with balanced class distribution for early detection of stunting and wasting,” Human Nutrition & Metabolism, vol. 42, p. 200340, 2025, doi: https://doi.org/10.1016/j.hnm.2025.200340.
[36] A. P. Joshi and B. V. Patel, “Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process,” Oriental journal of computer science and technology, vol. 13, no. 0203, pp. 78–81, Jan. 2021, doi: 10.13005/ojcst13.0203.03.
[37] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Mar. 29, 2021, Frontiers Media S.A. doi: 10.3389/fenrg.2021.652801.
[38] L. Wang and L. Liu, “Athlete body type recognition based on spectral co-clustering and CNN,” International Journal of Clothing Science and Technology, vol. 38, no. 3, pp. 389–414, 2026, doi: https://doi.org/10.1108/IJCST-04-2023-0055.
[39] V. P. Kothari and P. S. Chakurkar, “Towards safer environments: A YOLO and MediaPipe-based human fall detection system with alert automation,” MethodsX, vol. 15, Dec. 2025, doi: 10.1016/j.mex.2025.103623.
[40] A. Latreche, R. Kelaiaia, A. Chemori, and A. Kerboua, “Reliability and validity analysis of MediaPipe-based measurement system for some human rehabilitation motions,” Measurement (Lond)., vol. 214, Jun. 2023, doi: 10.1016/j.measurement.2023.112826.
[41] W. Simoes, L. Reis, C. Araujo, and J. Maia, “Accuracy Assessment of 2D Pose Estimation with MediaPipe for Physiotherapy Exercises,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 446–453. doi: 10.1016/j.procs.2024.11.132.
[42] M. A. Ummu Habeeba, P. Jayashree, M. Livitha Poornima, D. Saraswathi, G. Muralimohan, and S. Edward Jero, “FusionPose: a MediaPipe and keypoint R-CNN fused model for enhanced cyclist pose estimation and injury risk prediction,” Advanced Engineering Informatics, vol. 69, Jan. 2026, doi: 10.1016/j.aei.2025.104014.
Published
2026-10-16
How to Cite
[1]
A. H. RS, P. Purnamawati, Hendra Jaya, and Anas Arfandi, “AI-StuntDetect: Development of Early Stunting Detection System for Young Children Based on CNN and MediaPipe Pose”, j.electron.electromedical.eng.med.inform, vol. 8, no. 4, pp. 1587-1608, Oct. 2026.
Section
Medical Informatics
Copyright (c) 2026 Asmaul Husna RS, Purnamawati, Hendra Jaya, Anas Arfandi
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