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

TL;DR

PressureTransferNet is a new deep learning approach that synthesizes human-specific ground pressure profiles from different individuals, enhancing human activity recognition and related applications.

Contribution

It introduces a novel encoder-decoder model that transfers human attributes to pressure maps using simulated data, validated on real-world datasets.

Findings

Achieved a binary R-square of 0.79 on ground contact areas.

F1 score of 0.911±0.015 in classifying pressure maps.

Effectively transfers human attributes to pressure profiles across scenarios.

Abstract

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.