Oliinyk, V.Verkhovska, A.2026-03-032026-03-032026Oliinyk, V. Comparative Evaluation of Modern Computer Vision Models for Grasp Type Determination in Bionic Prostheses / V. Oliinyk, A. Verkhovska // Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2026. – № 1 (48). – С. 30-38. – Бібліогр.: 11 назв.https://ela.kpi.ua/handle/123456789/79192This research presents a comprehensive evaluation of some of the modern computer vision models for grasp type determination in bionic prostheses. The study leverages pretrained models, including CLIP and ResNet, with zero-shot and few-shot learning strategies to classify grasp types from object images. Several classification approaches, including Matching Networks, Prototypical Networks, and K-Nearest Neighbors, were evaluated in terms of top-1 and top-3 accuracy, as well as their inference time. The developed solution demonstrates promising results in limited-data settings and offers practical value for integration into modern intelligent prosthetic devices. We demonstrated the effectiveness of zero-shot and few-shot approaches for grasp type recognition. The simple zero-shot CLIP model in combination with K-Nearest Neighbors algorithm achieved the highest top-3 accuracy of 85%, exhibiting strong adaptability to previously unseen objects. This makes it particularly suitable for deployment in real-time, dynamic environments.enbionic prosthesiscomputer visiongrasp classificationfew-shot learningzero-shot recognitionCLIP modeldeep learningneural networksintelligent systemsPyTorchбіонічний протезкомп’ютерний зіркласифікація захватумашинне навчаннянейронні мережіfew-shot навчанняzero-shot розпізнаваннямережі відповідностіпрототипічні мережірозпізнавання об’єктівArticleС. 30-38https://doi.org/10.20535/1560-8956.48.2026.351880004.8