Development of a mobile application for testing fine motor skills disorders
Article Sidebar
Open full text
Issue Vol. 15 No. 1 (2025)
-
Statistical reliability of decisions on controlled process faults
Yevhen Volodarskyi, Oleh Kozyr, Zygmunt Warsza5-9
-
Pulse chaotic generator based a classical Chua’s circuit
Volodymyr Rusyn, Andrii Samila, Bogdan Markovych, Aceng Sambas, Christos Skiadas, Milan Guzan10-14
-
Stability of metaheuristic PID controllers in photovoltaic dc microgrids
Elvin Yusubov, Lala Bekirova15-21
-
Integrating numerical simulation and experimental data for enhanced structural health monitoring of bridges
Om Narayan Singh, Kaushik Dey22-26
-
Application of multi-agent programming for modeling the viscosity state of mash in alcohol production
Larysa Gumeniuk, Ludmyla Markina, Viktor Satsyk, Pavlo Humeniuk, Anton Lashch27-32
-
A stochastic interval algebra for smart factory processes
Piotr Dziurzanski, Konrad Kabala, Agnieszka Konrad33-38
-
Advancements in solar panel maintenance: a review of IoT-integrated automatic dust cleaning systems
Balamurugan Rangaswamy, Ramasamy Nithya39-44
-
Modified cosine-quadratic reflectance model
Oleksandr Romanyuk, Volodymyr Lytvynenko, Yevhen Zavalniuk45-48
-
Comparative analysis of lithium-iron-phosphate and sodium-ion energy storage devices
Huthaifa A. Al_Issa, Mohamed Qawaqzeh, Lina Hani Hussienat, Ruslan Oksenych, Oleksandr Miroshnyk, Oleksandr Moroz, Iryna Trunova, Volodymyr Paziy, Serhii Halko, Taras Shchur49-54
-
Investigation of DC-AC converter with microcontroller control of inverter frequency
Anatolii Tkachuk, Mykola Polishchuk, Liliia Polishchuk, Serhii Kostiuchko, Serhii Hryniuk, Liudmyla Konkevych55-61
-
Mathematical apparatus for finding the optimal configuration secure communication network with a specified number of subscribers
Volodymyr Khoroshko, Yuliia Khokhlachova, Oleksandr Laptiev, Al-Dalvash Ablullah Fowad62-66
-
Critical cybersecurity aspects for improving enterprise digital infrastructure protection
Roman Kvуetnyy, Volodymyr Kotsiubynskyi, Serhii Husak, Yaroslav Movchan, Nataliia Dobrovolska, Sholpan Zhumagulova, Assel Aitkazina67-72
-
Modification of the Peterson algebraic decoder
Dmytro Mogylevych, Iryna Kononova, Liudmyla Pogrebniak, Kostiantyn Lytvyn, Igor Gyrenko73-78
-
Development of a model for calculating the dilution of precision coefficients of the global navigation system at a given point in space
Oleksandr Turovsky, Nazarii Blazhennyi, Roman Vozniak, Yana Horbachova, Kostiantyn Horbachov, Nataliia Rudenko79-87
-
LLM based expert AI agent for mission operation management
Sobhana Mummaneni, Syama Sameera Gudipati, Satwik Panda88-94
-
Review of operating systems used in unmanned aerial vehicles
Viktor Ivashko, Oleh Krulikovskyi, Serhii Haliuk, Andrii Samila95-100
-
Optimization of machine learning methods for de-anonymization in social networks
Nurzhigit Smailov, Fatima Uralova, Rashida Kadyrova, Raiymbek Magazov, Akezhan Sabibolda101-104
-
Robust deepfake detection using Long Short-Term Memory networks for video authentication
Ravi Kishan Surapaneni, Hameed Syed, Harshitha Kakarala, Venkata Sai Srikar Yaragudipati105-108
-
Regional trending topics mining from real time Twitter data for sentiment, context, network and temporal analysis
Mousumi Hasan, Mujiba Shaima, Quazi Saad ul Mosaher109-116
-
Model development to improve the predictive maintenance reliability of medical devices
Khalid Musallam Alahmadi, Essam Rabea Ibrahim Mahmoud, Fitrian Imaduddin117-124
-
Explainable artificial intelligence for detecting lung cancer
Vinod Kumar R S, Bushara A R, Abubeker K M, Smitha K M, Abini M A, Jubaira Mammoo, Bijesh Paul125-130
-
Design and implementation of a vein detection system for improved accuracy in blood sampling
Omar Boutalaka, Achraf Benba, Sara Sandabad131-134
-
Metrological feature for determining the concentration of cholesterol, triglycerides, and phospholipids for psoriasis detection
Ivan Diskovskyi, Yurii Kachurak, Orysya Syzon, Marta Kolishetska, Bogdan Pinaiev, Oksana Stoliarenko135-138
-
Development of a mobile application for testing fine motor skills disorders
Marko Andrushchenko, Karina Selivanova, Oleg Avrunin, Alla Kraievska, Orken Mamyrbayev, Kymbat Momynzhanova139-143
-
Artificial intelligence in education: ChatGPT-based simulations in teachers’ preparation
Marina Drushlyak, Tetiana Lukashova, Volodymyr Shamonia, Olena Semenikhina144-152
-
CKSD: Comprehensive Kurdish-Sorani database
Jihad Anwar Qadir, Samer Kais Jameel, Wshyar Omar Khudhur, Kamaran H. Manguri153-156
Archives
-
Vol. 15 No. 3
2025-09-30 24
-
Vol. 15 No. 2
2025-06-27 24
-
Vol. 15 No. 1
2025-03-31 26
-
Vol. 14 No. 4
2024-12-21 25
-
Vol. 14 No. 3
2024-09-30 24
-
Vol. 14 No. 2
2024-06-30 24
-
Vol. 14 No. 1
2024-03-31 23
-
Vol. 13 No. 4
2023-12-20 24
-
Vol. 13 No. 3
2023-09-30 25
-
Vol. 13 No. 2
2023-06-30 14
-
Vol. 13 No. 1
2023-03-31 12
-
Vol. 12 No. 4
2022-12-30 16
-
Vol. 12 No. 3
2022-09-30 15
-
Vol. 12 No. 2
2022-06-30 16
-
Vol. 12 No. 1
2022-03-31 9
-
Vol. 11 No. 4
2021-12-20 15
-
Vol. 11 No. 3
2021-09-30 10
-
Vol. 11 No. 2
2021-06-30 11
-
Vol. 11 No. 1
2021-03-31 14
Main Article Content
DOI
Authors
Abstract
The purpose of the article is development of a mobile application to test graphic skills and disorders of fine motor skills determination. Many techniques are used to test fine motor skills, including drawing, cutting, folding, creating compositions from various materials, and more. However, using digital devices to test fine motor skills will be automatic, useful, and helpful. Digital devices such as sensor and graphic tablets, sensors, digitizers, and smartphones can provide accurate measurements of reaction times and movement speed, and allow real-time data to be recorded and analyzed. A cross-platform tool was developed to test basic graphic skills and level of fine motor skills development. The algorithm for the determination of the graphic abilities of users is based on the Frechette discrete distance formula, which allows measuring the deviation of an experimental figure from the etalon figure. Experimental results were conducted at the laboratory of 3D biomedical technologies of the Department of Biomedical Engineering of Kharkiv National University of Radio Electronics. The application is designed to analyze graphic skills through a series of exercises that test various skills, such as drawing, shading, and coloring shapes.
Keywords:
References
[1] Apple Inc. Vision Framework Overview. Apple Developer Documentation, 2024 [https://developer.apple.com/documentation/vision] (available: 9.08.2024).
[2] Avrunin O. et al.: Improving the methods for visualization of middle ear pathologies based on telemedicine services in remote treatment. 2020 IEEE KhPI Week on Advanced Technology, KhPI Week 2020, 347–350 [https://doi:10.1109/KhPIWeek51551.2020.9250090]. DOI: https://doi.org/10.1109/KhPIWeek51551.2020.9250090
[3] Fine Motor Skills Practice on the Play Store [https://play.google.com/store/apps/details?id=air.tracing] (available: 9.08.2024).
[4] Hartingsveldt M. J. et al.: Reliability and validity of the fine motor scale of the peabody developmental motor scales-2. Occupational Therapy International 12(1), 2005, 1–13 [https://doi.org/10.1002/oti.11]. DOI: https://doi.org/10.1002/oti.11
[5] KanDo: Fine Motor Skills Measurement Tool on the App Store. [https://apps.apple.com/us/app/kando-fine-motor-skills-measurement-tool/id499010991] (available: 9.08.2024).
[6] Mahmoud W. A. J.: Motor skill acquisition in children with poor motor coordination. Oxford Brookes University, 2017 [https://doi.org/10.24384/Z1P3-3A68].
[7] Martin R. C.: Clean Architecture: A Craftsman’s Guide to Software Structure and Design. Pearson, 2018.
[8] ML Kit, Google Developers [https://developers.google.com/ml-kit/guides] (available: 9.08.2024).
[9] Oberklaid F. et al.: Children’s health and development: Approaches to early identification and intervention. Archives of Disease in Childhood 98(12), 2013, 1008–1011 [https://doi.org/10.1136/archdischild-2013-304091]. DOI: https://doi.org/10.1136/archdischild-2013-304091
[10] Selivanova K. G. et al.: Biometric hand tremor identification on graphics tablet. Proc. SPIE 11176, 2019, 111762H [https://doi.org/10.1117/12.2536421]. DOI: https://doi.org/10.1117/12.2536421
[11] Selivanova K., Avrunin O.: Method of Hand Movement Disorders Determination based on the Surgeon's Laparoscopic Video Recording. 3rd KhPI Week on Advanced Technology – KhPI Week, 2022, 1–4 [https://doi.org/10.1109/KhPIWeek57572.2022.9916457]. DOI: https://doi.org/10.1109/KhPIWeek57572.2022.9916457
[12] Sierra C. et al.: Fine Motor Activities in Elementary School Children: A Replication Study. Am J Occup Ther 74(2), 2020, 7402345010p1–7402345010p7 [https://doi.org/10.5014/ajot.2020.035014]. DOI: https://doi.org/10.5014/ajot.2020.035014
[13] Strooband K. F. B. et al.: Systematic review and meta-analyses: Motor skill interventions to improve fine motor development in children aged brith to 6 years. Journal of Developmental Behavioral Pediatrics, 41(4), 2020, 319–331 [https://doi.org/10.1097/dbp.0000000000000779]. DOI: https://doi.org/10.1097/DBP.0000000000000779
[14] Strooband K. F. B. et al.: Revelance and risk factors of pre-schoolers' fine motor delay within vulnerable Australian communities. Journal of Paediatrics and Child Health 57(1), 2020, 114–120 [https://doi.org/10.1111/jpc.15152]. DOI: https://doi.org/10.1111/jpc.15152
[15] Taeger J. et al.: Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study. JMIR mHealth and uHealth 9(1), 2021, e193466 [https://doi.org/10.2196/19346]. DOI: https://doi.org/10.2196/19346
[16] Wójcik W. et al.: Information Technology in Medical Diagnostics II. Taylor & Francis Group. CRC Press, Balkema Book, London 2019. DOI: https://doi.org/10.1201/9780429057618
Article Details
Abstract views: 219

