Real-Time Recognition of Dual-Arm Motion Using Joint Direction Vectors and Temporal Deep Learning †

We developed a dual-arm motion recognition system designed for real-time upper-limb movement analysis using video input. The system integrates MediaPipe Hands for skeletal critical point detection, a feature extraction pipeline that encodes spatial and temporal characteristics from upper-limb joints, and a three-layer long short-term memory network for temporal modeling and classification. By computing directional vectors from the shoulder to the elbow and wrist, a 168-dimensional feature vector is generated per frame. Sequences of 90 frames are used to capture full motion patterns. The system effectively supports multi-class recognition of coordinated dual-arm gestures, offering applications in rehabilitation, gesture control, and human–computer interaction.