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This paper proposes a domain adaptation approach for handwriting trajectory reconstruction from IMU sensors, addressing signal differences between adult and children's handwriting to improve reconstruction accuracy for educational applications.
A new mixture-of-experts approach for reconstructing handwriting trajectories from IMU sensor data, with separate experts for touching and hovering phases, and a new public benchmark dataset.