  We propose PressureTransferNet, a novel method for Human Activity Recognition
(HAR) using ground pressure information. Our approach generates body-specific
dynamic ground pressure profiles for specific activities by leveraging existing
pressure data from different individuals. PressureTransferNet is an
encoder-decoder model taking a source pressure map and a target human attribute
vector 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 with
various human attributes and pressure profiles. Evaluation on a real-world
dataset shows its effectiveness in accurately transferring human attributes to
ground pressure profiles across different scenarios. We visually confirm the
fidelity of the synthesized pressure shapes using a physics-based deep learning
model 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 physical
pressure mat data demonstrates the correctness of the synthesized pressure
maps, making our method valuable for data augmentation, denoising, sensor
simulation, and anomaly detection. Applications span sports science,
rehabilitation, and bio-mechanics, contributing to the development of HAR
systems.
