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We propose PressureTransferNet, a novel method for Human Activity Recognition(HAR) using ground pressure information. Our approach generates body-specificdynamic ground pressure profiles for specific activities by leveraging existingpressure data from different individuals. PressureTransferNet is anencoder-decoder model taking a source pressure map and a target human attributevector as inputs, producing a new pressure map reflecting the target attribute.To train the model, we use a sensor simulation to create a diverse dataset withvarious human attributes and pressure profiles. Evaluation on a real-worlddataset shows its effectiveness in accurately transferring human attributes toground pressure profiles across different scenarios. We visually confirm thefidelity of the synthesized pressure shapes using a physics-based deep learningmodel and achieve a binary R-square value of 0.79 on areas with ground contact.Validation through classification with F1 score (0.911$\pm$0.015) on physicalpressure mat data demonstrates the correctness of the synthesized pressuremaps, making our method valuable for data augmentation, denoising, sensorsimulation, and anomaly detection. Applications span sports science,rehabilitation, and bio-mechanics, contributing to the development of HARsystems.

PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps