REAL-TIME RECOGNITION OF ROTATIONAL HANDMOVEMENTS USING MEDIAPIPE AND OPENCV

Yenni Astuti (Department of Electrical Engineering, Institut Teknologi Dirgantara Adisutjipto)
Maria Asumpta Deny Kusumaningrum (Department of Electrical Engineering, Institut Teknologi Dirgantara Adisutjipto)
Anggraini Kusumaningrum (Department of Informatics)
Riani Nurdin (Department of Industrial Engineering)
Mayang Sativa (Department of Electrical Engineering, Institut Teknologi Dirgantara Adisutjipto)
Nur Priyanto Poupon (Institut Teknologi Dirgantara Adisutjipto)
Tutiaditama (Indonesian National Military Air Force)

Jurnal TNI Angkatan Udara
Vol 5 No 4 (2026): Jurnal TNI Angkatan Udara Triwulan Keempat

This study proposes a real-time gesture recognition system for detecting rotational hand movements (pitch, yaw, and roll) using MediaPipe and OpenCV for extracting features and classifying gestures. The method extracts angular features from hand landmarks and classifies gestures via a lightweight rule-based approach. The system was tested under twelve environmental scenarios, combining four distances (50 – 450 cm) and three lighting conditions, with 1,500 gesture samples from 10 participants. Results show 100% accuracy at 50 cm and 200 cm across all lighting conditions, with up to 93.3% accuracy at 300 cm, surpassing similar rule-based systems in performance. These findings demonstrate a low-cost, contactless solution for interactive systems