Visión artificial en dispositivos móviles: una revisión de innovaciones y aplicaciones recientes.
DOI:
https://doi.org/10.65015/4drxnc60Palabras clave:
Visión computacional, Desarrollo de aplicaciones móviles, Teléfonos inteligentesResumen
La visión computacional en dispositivos móviles está transformando múltiples sectores al ofrecer nuevas formas de interacción y acceso a tecnologías avanzadas. Su integración en los teléfonos inteligentes ha permitido el desarrollo de aplicaciones con un impacto social y tecnológico significativo en áreas como la salud, la nutrición y la movilidad. En éste artículo analizamos las innovaciones recientes en el uso de la visión computacional móvil y su influencia en diferentes ámbitos sociales y productivos para proporcionar a investigadores en visión artificial y desarrolladores de apps móviles, información sobre los avances en el área para inspirar la creación de nuevas aplicaciones innovadoras, mejorar la eficiencia de las existentes y fomentar el desarrollo de tecnologías que puedan tener un impacto positivo en la vida cotidiana y en diversas industrias. Se realizó una revisión bibliográfica de publicaciones científicas comprendidas entre 2020 y 2025, obtenidas de bases de datos especializadas. Los estudios seleccionados se enfocan en aplicaciones en salud, nutrición, discapacidad visual o motora, deportes, conducción, comercio, pesca, veterinaria y agricultura. Los hallazgos muestran un crecimiento sostenido en las aplicaciones de visión computacional móvil, con avances notables en el reconocimiento de imágenes, el diagnóstico médico y el monitoreo inteligente.
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Abbas, A., Schultebraucks, K., & Galatzer-Levy, I. R. (2021). Digital measurement of mental health: challenges, promises, and future directions. Psychiatric Annals, 51(1), 14-20, doi: 10.1101/2020.07.20.20158287
Adetiba, E., Ajayi, O. T., Kala, J. R., Badejo, J. A., & Abayomi, A. (2021). LeafsnapNet: an experimentally evolved deep learning model for recognition of plant species based on leafsnap image dataset.
Alasmary, W. (2020). An innovative smartphone-based solution for traffic rule violation detection. International Journal of Advanced Computer Science and Applications, 11(1). doi: 10.14569/IJACSA.2020.0110177
Aleynikov, A. & Toropov, V.. (2021). App for smartphone for detecting fungus diseases of plant leaves. doi: 10.26898/0370-8799-2021-2-11
Alizadeh, N., Salimi, A., & Hallaj, R. (2020). A strategy for visual optical determination of glucose based on a smartphone device using fluorescent boron-doped carbon nanoparticles as a light-up probe. Microchimica Acta, 187, 1-10. doi: 10.1007/S00604-019-3871-1
Amani, H., Badak-Kerti, K., & Mousavi Khaneghah, A. (2022). Current progress in the utilization of smartphone-based imaging for quality assessment of food products: A review. Critical reviews in food science and nutrition, 62(13), 3631-3643. doi: 10.1080/10408398.2020.1867820
Anthimopoulos, M., Dehais, J., Shevchik, S., Ransford, B. H., Duke, D., Diem, P., & Mougiakakou, S. (2015). Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones. Journal of diabetes science and technology, 9(3), 507-515. doi: 10.1177/1932296815580159
Balsalobre-Fernández, C., Geiser, G., Krzyszkowski, J., & Kipp, K. (2020). Validity and reliability of a computer-vision-based smartphone app for measuring barbell trajectory during the snatch. Journal of Sports Sciences, 38(6), 710–716. https://doi.org/10.1080/02640414.2020.1729453
Balcerek, J., Hinc, M., Jalowski, Ł., Michalak, J., Rabiza, M., & Konieczka, A. (2019, September). Vision-based mobile application for supporting the user in the vehicle operation. In 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 250-255). IEEE. doi: 10.23919/SPA.2019.8936754
Bastidas-Guacho, G. K., Paguay Alvarado, M. A., Moreno-Vallejo, P. X., Moreno-Costales, P. R., Ocaña Yanza, N. S., & Troya Cuestas, J. C. (2025). Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals. Multimodal Technologies and Interaction, 9(5), 48.
Billingsley, J. (2021). Vision Guidance with a Smart-Phone. Mechatronics and Machine Vision in Practice 4, 81-85. doi: 10.1007/978-3-030-43703-9_7
Contreras-Salazar, K., Costa-Mondragon, P., & Ugarte, W. (2025). Mobile Application for Optimizing Exercise Posture Through Machine Learning and Computer Vision in Gyms. Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru.
Chethan, K. S., Donepudi, S., Supreeth, H. V., & Maani, V. D. (2021). Mobile application for classification of plant leaf diseases using image processing and neural networks. In Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2020 (pp. 287-306). Springer Singapore. doi: 0.1007/978-981-15-8530-2_22
Dainelli, R., Bruno, A., Martinelli, M., Moroni, D., Rocchi, L., Morelli, S., ... & Toscano, P. (2024). GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat. Frontiers in Plant Science, 15, 1298791.
Dwivedi, A., Henige, M., Anklam, K., & Döpfer, D. (2025). Real-time digital dermatitis detection in dairy cows on Android and iOS apps using computer vision techniques. Translational Animal Science, 9, txae168.
Dylan, L. A. N. E., & Rezaei, S (2020). Actuation of vehicle systems using device-as-key and computer vision techniques.
Elia, N., Monge, D., Kinzel, C., & Muheidat, F. (2020, December). StimulEye: A Computer Vision Based Concussion Detector. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1624-1628). IEEE. 10.1109/CSCI51800.2020.00299
Eugenio, A. M., Patulot, M. J., Seguiro, L. J., Tuazon, A. N., Huyo-a, S. L., Abisado, M., & Sampedro, G. A. (2023, February). EyeRis: Visual Image Recognition using Machine Learning for the Visually-Impaired. In 2023 International Conference on Electronics, Information, and Communication (ICEIC) (pp. 1-5). IEEE. doi: 10.1109/ICEIC57457.2023.10049927
Ferraz, S. A. G. (2020). Utilização da câmara smartphone para monitorizar a aderência à terapia inalatória (Master's thesis, Instituto Politecnico do Porto (Portugal)).
Fialho, R., Moreira, R., Santos, T. C., Vasconcelos, S. S., Teixeira, S., Silva, F., ... & Teles, A. S. (2021, June). Can computer vision be used for anthropometry? A feasibility study of a smart mobile application. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 119-124). IEEE. doi: 10.1109/CBMS52027.2021.00058
Forsyth, D. A., & Ponce, J. (2011). Computer vision: a modern approach. Pearson.
Szeliski, R. (2022). Computer vision: algorithms and applications. Springer Nature
Freitas, U., Pache, M., Gonçalves, W., Matsubara, E., Sabino, J., Sant'Ana, D., & Pistori, H. (2020, October). Analysis of color feature extraction techniques for Fish Species Identification. In Anais do XVI Workshop de Visão Computacional (pp. 140-145). SBC doi: 10.5753/WVC.2020.13495
Fusco, G., & Coughlan, J. M. (2020, April). Indoor localization for visually impaired travelers using computer vision on a smartphone. In Proceedings of the 17th international web for all conference (pp. 1-11). doi: 10.1145/3371300.3383345
Gu, J. (2022, October). Advances of Computational Imaging on Mobile Phones. In Proceedings of the 2nd International Workshop on Robust Understanding of Low-quality Multimedia Data: Unitive Enhancement, Analysis and Evaluation (pp. 5-5).
Herzig, D., Nakas, C. T., Stalder, J., Kosinski, C., Laesser, C., Dehais, J., ... & Bally, L. (2020). Volumetric food quantification using computer vision on a depth-sensing smartphone: preclinical study. JMIR mHealth and uHealth, 8(3), e15294.doi: 10.2196/15294
Hung, H. V., Nguyen, L. T., Nguyen, H. P., Tram, N. T., & Danh, L. V. Q. (2023, May). A Low-Cost Computer Vision Approach for Counting Juvenile Shrimp Using OpenCV and Smartphone. In International Conference on Business and Technology (pp. 413-423). Cham: Springer Nature Switzerland.
Kasthuri, A. M., & Kesavan, M. B. Advanced Blind helper Android Application Using Text-to-speech synthesis. https://doi.org/10.22214/ijraset.2023.49861
Khanal, B., Poudel, P., Chapagai, A., Regmi, B., Pokhrel, S., & Khanal, S. R. (2024). Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease. arXiv preprint arXiv:2412.05996.
Khng, S. R. H. (2023). Smartphone-based remote security monitoring.
Krishnappa Babu, P. R., Di Martino, J. M., Aiello, R., Eichner, B., Espinosa, S., Green, J., ... & Sapiro, G. (2024). Validation of a Mobile App for Remote Autism Screening in Toddlers. NEJM AI, 1(10), AIcs2400510.
Lawin, F. J., Byström, A., Roepstorff, C., Rhodin, M., Almlöf, M., Silva, M., ... & Hernlund, E. (2023). Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture. Animals, 13(3), 390 doi: 10.3390/ani13030390
Leechaikul, N., & Charoenseang, S. (2021). Computer vision based rehabilitation assistant system. In Intelligent Human Systems Integration 2021: Proceedings of the 4th International Conference on Intelligent Human Systems Integration (IHSI 2021): Integrating People and Intelligent Systems, February 22-24, 2021, Palermo, Italy (pp. 408-414). Springer International Publishing. doi: 0.1007/978-3-030-68017-6_61
Leonida, A. M. G., & Caballero, A. R. (2022, May). Aleaf: An Android-Based Phytotherapy Leaf Recognition Using Custom Vision Machine Learning. In 2022 7th International Conference on Business and Industrial Research (ICBIR) (pp. 488-493). IEEE. doi: 10.1109/ICBIR54589.2022.9786509
Lukichev, A. N., & Doroshin, S. Y. (2021, January). Estimating the Cost of a Smartphone by Photo. In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) (pp. 507-512). IEEE.
Mahendran, J. K., Barry, D. T., Nivedha, A. K., & Bhandarkar, S. M. (2021). Computer vision-based assistance system for the visually impaired using mobile edge artificial intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2418-2427). doi: 10.1109/CVPRW53098.2021.00274
Madden, K. M. (2021). Towards Verifying Smartphone Users Via Gripping Hand Image Classification.
Martinez‐Alpiste, I., Casaseca‐de‐la‐Higuera, P., Alcaraz‐Calero, J. M., Grecos, C., & Wang, Q. (2020). Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles. Journal of Field Robotics, 37(3), 404-420.
May, A., Gesell‐May, S., Müller, T., & Ertel, W. (2022). Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis. Equine veterinary journal, 54(5), 847-855. 10.1111/EVJ.13528
Mone, S., Salunke, N., Jadhav, O., Barge, A., & Magar, N. (2021). Machine Learning Based Computer Vision Application for Visually Disabled People. Int J Sci Res Comput Sci Eng Inf Technol, 7(3), 488-494. doi: 10.32628/CSEIT2173130
Muthireddy, V., & Jawahar, C. V. (2021). Computer vision for capturing flora. Digital Techniques for Heritage Presentation and Preservation, 245-272 doi: 10.1007/978-3-030-57907-4_12
Perera, G. S. T., Madhubhashini, K. W. R., Lunugalage, D., Piyathilaka, D. V. S., Lakshani, W. H. U., & Kasthurirathna, D. (2020, December). Computer vision based indoor navigation for shopping complexes. In Proceedings of the 2020 4th international conference on vision, image and signal processing (pp. 1-6).
Patra, S., Van Hamme, D., Veelaert, P., Calafate, C. T., Cano, J. C., Manzoni, P., & Zamora, W. (2020). Detecting vehicles’ relative position on two-lane highways through a smartphone-based video overtaking aid application. Mobile Networks and Applications, 25, 1084-1094. doi 10.1007/S11036-020-01526-2
Pineda Medina, D., Miranda Cabrera, I., de la Cruz, R. A., Guerra Arzuaga, L., Cuello Portal, S., & Bianchini, M. (2024). A Mobile App for Detecting Potato Crop Diseases. Journal of Imaging, 10(2), 47.
Phong, L. H., Truong, N. P., Danh, L. V. Q., Nam, V. H., Tung, N. T., & Dung, T. T. (2021, April). A Portable System for Automated Measurement of Striped Catfish Length Using Computer Vision. In International Conference on Ubiquitous Computing and Intelligent Information Systems (pp. 607-618). Singapore: Springer Nature Singapore 10.1007/978-981-19-2541-2_50
Qashlim, A., Basri, B., Haeruddin, H., Ardan, A., Nurtanio, I., & Ilham, A. (2020). Smartphone technology applications for milkfish image segmentation using opencv library. doi: 10.3991/IJIM.V14I08.12423
Rafiq, R. B., Hoque, K. M., Kabir, M. A., Ahmed, S., & Laird, C. (2022). Optifit: Computer-vision-based smartphone application to measure the foot from images and 3d scans. Sensors, 22(23), 9554. doi: 10.3390/s22239554
Ralf, E., Harskamp., H., C., deVijlder., Marcel, W, Bekkenk. (2022). Smartphone apps for self-diagnosis of skin cancer.. Nederlands Tijdschrift voor Geneeskunde.
Ramalingam, D., Tiwari, S., Seth, H. (2020). Vision Connect: A Smartphone Based Object Detection for Visually Impaired People. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_92
Reda, M., Suwwan, R., Alkafri, S., Rashed, Y., & Shanableh, T. (2022, July). Agroaid: A mobile app system for visual classification of plant species and diseases using deep learning and tensorflow lite. In Informatics (Vol. 9, No. 3, p. 55). MDPI. Doi: 10.3390/informatics9030055
Sarkar, P. & Siddiqui, U. (2020). Computer vision and its role in driving safety. International Journal of Advance Research, Ideas and Innovations in Technology,
Sathish, B. S., Bhuvan, R., Suhas, B. S., Popli, T., Spoorthi, R., & Hebbar, R. R. (2023, May). An Advanced Computer Vision Techniques for Object Recognition and Navigation Assistance for the Visually Impaired. In 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN) (pp. 1-6). IEEE.
Shukurov, J. (2024). Improve accessibility for Low Vision and Blind people using Machine Learning and Computer Vision. arXiv preprint arXiv:2404.00043.
Tigua, M. F. C., Riofrio, J. A. S., & Sanango, H. G. (2023). Desarrollo de Aplicación Móvil con la Identificación Morfológica de Especies Vegetales.: Development of Mobile Application with Morphological Identification of Plant Species. Revista Científica Multidisciplinar G-nerando, 4(2), 870-882.
Thomanek, R., Platte, B., Baumgart, M., Roschke, C., & Ritter, M. (2023). AI-powered real-time analysis of human activity in videos via smartphone. Cognitive Computing and Internet of Things, 73(73). 10.54941/ahfe1003972
Sumaiya, J., Hasan, M. R., & Hossain, E. (2020, December). Noninvasive blood glucose measurement using live video by smartphone. In 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC) (pp. 1-6). IEEE. doi: 10.1109/R10-HTC49770.2020.9357018
Uéliton, de, Paula, Freitas. (2014). Identificação de espécies de peixes utilizando histogramas de palavras visuais em imagens coloridas.
Vasudevan, V., Thanmai, T., Yadav, R., Reddy, P.U.S., Pradhan, S. (2024). Eye Support: AI Enabled Support System for Visually Impaired Senior Citizens. In: Aurelia, S., J., C., Immanuel, A., Mani, J., Padmanabha, V. (eds) Computational Sciences and Sustainable Technologies. ICCSST 2023. Communications in Computer and Information Science, vol 1973. Springer, Cham. https://doi.org/10.1007/978-3-031-50993-3_38
Wang, Z., Liu, K., Wang, J., Xu, J., Chen, J., Jin, Y., & Slavin, R. (2022, December). A Vision-Based Low-Cost Power Wheelchair Assistive Driving System for Smartphones. In 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys) (pp. 1979-1986). IEEE. doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00295
