In human activity recognition (HAR), the availability of substantial ground
truth is necessary for training efficient models. However, acquiring ground
pressure data through physical sensors itself can be cost-prohibitive,
time-consuming. To address this critical need, we introduce Text-to-Pressure
(T2P), a framework designed to generate extensive ground pressure sequences
from textual descriptions of human activities using deep learning techniques.
We show that the combination of vector quantization of sensor data along with
simple text conditioned auto regressive strategy allows us to obtain
high-quality generated pressure sequences from textual descriptions with the
help of discrete latent correlation between text and pressure maps. We achieved
comparable performance on the consistency between text and generated motion
with an R squared value of 0.722, Masked R squared value of 0.892, and FID
score of 1.83. Additionally, we trained a HAR model with the the synthesized
data and evaluated it on pressure dynamics collected by a real pressure sensor
which is on par with a model trained on only real data. Combining both real and
synthesized training data increases the overall macro F1 score by 5.9 percent.

利用深度学习技术，使用文本描述生成大量的地面压力序列，通过离散潜在相关性获得由文本描述生成的高质量压力序列，并在实际压力传感器收集的压力动态上评估与仅使用真实数据训练的模型相当的 HAR 模型，将真实数据和合成的训练数据结合，使整体宏 F1 分数提高 5.9%。