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.

翻译：我们提出PressureTransferNet——一种利用地面压力信息进行人体活动识别（HAR）的创新方法。该方法通过利用不同个体的现有压力数据，生成特定活动对应的体态特异性动态地面压力分布。PressureTransferNet采用编码器-解码器架构，以源压力图与目标人体属性向量为输入，输出反映目标属性的全新压力图。为训练模型，我们运用传感器模拟技术构建包含多样化人体属性与压力分布的数据集。在真实数据集上的评估表明，该方法能有效将人体属性迁移至不同场景下的地面压力分布。通过基于物理的深度学习模型进行可视化验证，我们确认了合成压力形态的保真度，并在与地面接触区域实现0.79的二元R平方值。基于物理压力垫数据的分类验证（F1分数为0.911±0.015）进一步证明合成压力图的准确性，使该方法在数据增强、去噪、传感器模拟及异常检测等领域具有应用价值。其应用范围涵盖运动科学、康复医学与生物力学，为人体活动识别系统的发展提供支撑。