Development of a Portable Ultrasonic Digital Anthropometry System with Automated CIAF Classification
Downloads
Child malnutrition, particularly stunting, remains a critical public health issue with long-term effects on growth and cognitive development. In many community health settings, conventional anthropometric measurement tools used in community health services often present challenges, including human measurement error, non-standard data recording, and a lack of real-time diagnostic output. This study aims to develop and validate a portable ultrasonic sensor-based digital anthropometric system capable of automatically detecting the Composite Index of Anthropometric Failure (CIAF) in real-time. The novelty of this research lies in integrating non-contact ultrasonic height measurement with automated CIAF classification based on WHO 2006 growth standards, cloud-connected data storage, and a user-friendly interface designed for community health workers. This Research and Development study involved system design, laboratory calibration, field validation, and user acceptability testing. A total of 80 toddlers and 30 users (midwives, nutritionists, and Posyandu cadres) participated across regions with low and high stunting prevalence. Measurement accuracy was compared to gold-standard anthropometry, while usability was assessed through a Likert-scale evaluation. Laboratory tests indicated measurement error ranging from 0.0 to 0.2 cm, indicating high sensor precision. Field tests showed a mean difference of ≤1 cm with no statistically significant difference (p>0.05) compared to standard measurement. User evaluation reported high satisfaction, particularly in ease of use (92%), accuracy (90%), and program support benefit (94%). The developed portable ultrasonic digital anthropometry system provides accurate, fast, and standardized CIAF-based malnutrition detection, supporting more efficient child growth monitoring programs. The tool demonstrates strong potential for integration into community-based nutrition surveillance and national health information systems.
[1] UNICEF, World Health Organization, and The World Bank, Joint Malnutrition Estimates: Levels and Trends 2023 Edition, New York/Geneva/Washington DC: UNICEF/WHO/World Bank, 2023. [Online]. Available: https://data.unicef.org/resources/jme-report-2023/ (accessed Nov. 9, 2023).
[2] Badan Kebijakan Pembangunan Kesehatan, Survey Kesehatan Indonesia (SKI) 2023, Jakarta: Kementerian Kesehatan RI, 2023, pp. 1–68.
[3] T. Siswati, M. Primiaji Rialihanto, and B. A. Paramashanti, “Non-contact measurement using ultrasonic technology for human anthropometric: A narrative review,” J. Sci. Res. Rep., vol. 29, no. 7, pp. 1–7, 2023, doi: 10.9734/JSRR/2023/v29i71754.
[4] L. R. Rodríguez, M. S. SanSegundo, R. F. Cascales, N. G. D’Urso, A. Z.-Martí, and J. A. Hurtado-Sánchez, “Comparison of body scanner and manual anthropometric measurements of body shape: A systematic review,” Int. J. Environ. Res. Public Health, vol. 18, no. 1, 2021, doi: 10.3390/ijerph180100xx.
[5] G. D. Ceniccola et al., “Current technologies in body composition assessment: Advantages and disadvantages,” Nutrition, vol. 62, pp. 25–31, 2019, doi: 10.1016/j.nut.2018.11.028.
[6] R. S. Thiebaud, T. Abe, J. P. Loenneke, E. Fujita, and T. Akamine, “Body fat percentage assessment by ultrasound subcutaneous fat thickness measurements in middle-aged and older adults,” Clin. Nutr., vol. 38, no. 6, pp. 2659–2667, 2019, doi: 10.1016/j.clnu.2018.11.017.
[7] J. C. Pineau and M. Bouslah, “Prediction of body fat in male athletes from ultrasound and anthropometric measurements versus DXA,” J. Sports Med. Phys. Fitness, vol. 60, no. 2, pp. 251–256, 2020, doi: 10.23736/S0022-4707.19.09985-7.
[8] L. J. Jilantikiri, S. A. Yahaya, T. M. Ajibola, S. Oluwajoba, and C. A. Obioha, “Development of a digital body mass index (BMI) measuring device for low-resource settings,” J. Low-Resource Technol., vol. 4, no. 2, pp. 44–57, 2023.
[9] U. Umiatin, W. Indrasari, T. Taryudi, and A. F. Dendi, “Development of a multisensor-based non-contact anthropometric system for early stunting detection,” J. Sens. Actuator Netw., vol. 11, no. 4, 2022, doi: 10.3390/jsan11040069.
[10] Y. I. A. A. Ningrum, “Early detection of stunting based on feature engineering and machine learning algorithm approach,” IJEEMI, vol. 6, no. 3, pp. 147–155, 2024.
[11] J. O. Totosy de Zepetnek et al., “Test–retest reliability and validity of body composition methods in adults,” Clin. Physiol. Funct. Imaging, vol. 41, no. 5, pp. 417–425, 2021, doi: 10.1111/cpf.12716.
[12] M. Wigati, A. N. Nurlita, I. M. A. Gunawan, N. Y. Hendarta, M. Hasanbasri, and S. Helmyati, “Anthropometric kit development for stunted early detection among children under-two years old: Providing a portable body length measurer,” Open Access Maced. J. Med. Sci., vol. 10, no. E, pp. 852–859, 2022, doi: 10.3889/oamjms.2022.8952.
[13] W. Huo, X. Yuan, X. Li, W. Luo, J. Xie, and B. Shi, “Increasing acceptance of medical AI: The role of medical staff participation in AI development,” Int. J. Med. Inform., vol. 175, art. no. 105073, 2023, doi: 10.1016/j.ijmedinf.2023.105073.
[14] M. A. Hertzog, “Considerations in determining sample size for pilot studies,” Res. Nurs. Health, vol. 31, no. 2, pp. 180–191, 2008, doi: 10.1002/nur.20247.
[15] World Health Organization, WHO Child Growth Standards, Geneva: WHO, 2006. [Online]. Available: https://www.who.int/tools/child-growth-standards
[16] Kementerian Kesehatan Republik Indonesia, Peraturan Menteri Kesehatan Republik Indonesia Nomor 2 Tahun 2020 tentang Standar Antropometri Anak, Jakarta: Kemenkes RI, 2020. [Online]. Available: http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__2_Th_2020_ttg_Standar_Antropometri_Anak.pdf
[17] E. Mocini et al., “Digital anthropometry: A systematic review on precision, reliability and accuracy of most popular existing technologies,” Nutrients, vol. 15, no. 2, art. no. 302, 2023, doi: 10.3390/nu15020302.
[18] S. B. Heymsfield, B. Bourgeois, B. K. Ng, M. J. Sommer, X. Li, and J. A. Shepherd, “Digital anthropometry: A critical review,” Eur. J. Clin. Nutr., vol. 72, no. 5, pp. 680–687, 2018, doi: 10.1038/s41430-018-0145-7.
[19] G. M. Tinsley, M. L. Moore, J. R. Dellinger, B. T. Adamson, and M. L. Benavides, “Digital anthropometry via three-dimensional optical scanning: Evaluation of four commercially available systems,” Eur. J. Clin. Nutr., vol. 74, no. 7, pp. 1054–1064, 2020, doi: 10.1038/s41430-019-0526-6.
[20] B. Smith, C. McCarthy, M. E. Dechenaud, M. C. Wong, J. A. Shepherd, and S. B. Heymsfield, “Anthropometric evaluation of a 3D scanning mobile application,” Obesity, vol. 30, no. 6, pp. 1181–1188, 2022, doi: 10.1002/oby.23434.
[21] J. Kim et al., “Preclinical trial of noncontact anthropometric measurement using IR-UWB radar,” Sci. Rep., vol. 12, no. 1, art. no. 12209, 2022, doi: 10.1038/s41598-022-12209-1.
[22] H. Shen, H. Zhao, and Y. Jiang, “Machine learning algorithms for predicting stunting among under-five children in Papua New Guinea,” Children, vol. 10, no. 10, art. no. 1638, 2023, doi: 10.3390/children10101638.
[23] M. K. Ayele, G. A. Baye, S. H. Yesuf, A. A. Engda, and E. T. Mitiku, “Predicting stunting status among under-five children in Ethiopia using ensemble machine learning algorithms,” Sci. Rep., vol. 15, no. 1, art. no. 27907, 2025, doi: 10.1038/s41598-025-03206-1.
[24] G. Dumlu Bilgin et al., “Deciphering the concurrent phenomenon of childhood malnutrition using the extended composite index of anthropometric failure (ECIAF): Facts from the BESLEN project,” Public Health Nutr., vol. 28, no. 1, art. no. e9, 2024, doi: 10.1017/S1368980024002520.
[25] M. Briguglio et al., “Anthropometric measurements from a 3D photogrammetry-based digital avatar: A non-experimental cross-sectional study to assess reliability and agreement,” Appl. Sci., vol. 15, no. 10, art. no. 5738, 2025, doi: 10.3390/app15105738.
[26] WHO Multicentre Growth Reference Study Group, WHO Child Growth Standards: Length/Height-for-Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development, Geneva: World Health Organization, 2006.
[27] M. de Onis, A. W. Onyango, E. Borghi, A. Siyam, and J. Siekmann, “Development of a WHO growth reference for school-aged children and adolescents,” Bull. World Health Organ., vol. 85, no. 9, pp. 660–667, 2007, doi: 10.2471/BLT.07.043497.
[28] D. Chan, M. C. Chua, M. Hadimaja, S. Mukherjee, J. Wong, and F. Yap, “Artificial intelligence-driven anthropometric assessment for young children: Evaluating the accuracy and practicality of a digital image-based length and weight prediction tool,” BMJ Health Care Inform., 2025, doi: 10.1136/bmjhci-2025-101540.
[29] E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nat. Med., vol. 25, no. 1, pp. 44–56, 2019, doi: 10.1038/s41591-018-0300-7.
[30] T. Schmitz et al., “Effectiveness of an electronic clinical decision support system in improving the management of childhood illness in primary care in rural Nigeria: An observational study,” BMJ Open, vol. 12, no. 2, art. no. e055315, 2022, doi: 10.1136/bmjopen-2021-055315.
[31] J. Haskew, M. Ro, S. S. Turner, Y. Kim, S. S. S. Moon, and A. S. Chan, “Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya,” Int. J. Med. Inform., vol. 84, no. 5, pp. 349–354, 2015, doi: 10.1016/j.ijmedinf.2015.01.005.
[32] World Health Organization, “Digital health,” 2024. [Online]. Available: https://www.who.int/health-topics/digital-health
[33] M. McLaughlin, L. Metiboba, A. Giwa, O. F. Ojo, and N. Ravi, “Adherence to integrated management of childhood illness (IMCI) guidelines by community health workers in Kano State, Nigeria through use of a clinical decision support (CDS) platform,” BMC Health Serv. Res., vol. 24, art. no. 11245, 2024, doi: 10.1186/s12913-024-11245-z.
[34] T. Siswati, B. A. Paramashanti, L. Waris, N. Setiyawati, and A. Wijanarka, “Acceptability of a stuntingmeter digital ultrasonic: A mixed methods approach,” Malays. J. Med. Health Sci., vol. 20, suppl. 9, pp. 209–215, 2024, doi: 10.47836/mjmhs20.s9.34.
[35] K. D. Tzimourta, “Human-centered design and development in digital health: Approaches, challenges, and emerging trends,” Cureus, vol. 17, no. 6, art. no. e85897, 2025, doi: 10.7759/cureus.85897.
[36] P. R. Pognon, F. Boima, and Z. A. Mekonnen, “Health workers’ acceptance and satisfaction on the usability of digital health goods in Kono District, Sierra Leone,” BMC Health Serv. Res., vol. 25, art. no. 267, 2025.
[37] N. Alotaibi, C. B. Wilson, and M. Traynor, “Enhancing digital readiness and capability in healthcare: A systematic review of interventions, barriers, and facilitators,” BMC Health Serv. Res., vol. 25, art. no. 500, 2025, doi: 10.1186/s12913-025-12663-3.
[38] World Health Organization, WHO Guideline: Recommendations on Digital Interventions for Health System Strengthening, Geneva: WHO, 2019.
[39] D. A. Prihanggara and L. S. Handini, “The effectiveness of integrated growth monitoring and nutritional surveillance for early detection and prevention of malnutrition in early childhood: A literature review,” J. Divers. Med. Res., vol. 2, no. 3, pp. 91–96, 2025, doi: 10.33005/jdiversemedres.v2i3.105.
[40] J. d. A. Silva et al., “Digital health and primary health care quality: A survey case study,” Int. J. Environ. Res. Public Health, vol. 22, no. 7, art. no. 1015, 2025, doi: 10.3390/ijerph22071015.
[41] S. Carsley, C. S. Birken, P. Parkin, E. Pullenayegum, and K. Tu, “Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care,” J. Innov. Health Inform., vol. 25, no. 1, p. 963, 2018, doi: 10.14236/jhi.v25i1.963
[42] World Health Organization, “Digital health,” 2021. [Online]. Available: https://www.who.int/health-topics/digital-health (accessed Dec. 30, 2024).
Copyright (c) 2025 TRI SISWATI, Muhammad Primiaji Rialihanto, Bunga Astria Paramashanti, Farit Ardiyanto, Jutharat Attawet (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
