Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2580-2583. doi: 10.1109/EMBC46164.2021.9629684.
Analyzing human gait from plantar pressure is critical for human health. The majority of works focus on classifying the healthy plantar pattern from unhealthy ones. Different from previous works, we adopt a generative adversarial network to produce healthy plantar pressure image for individual patients. In this work, we do not have pairs of images for training thus we cast the problem as an unsupervised generative adversarial learning task. Our network benefits from multiple components: an encoder-decoder generator, a convolution-based discriminator, a convolution-based evaluation network, and a new term in the loss function to preserve the person's gait style. Our method achieves high performance (99.8%) on the CAD WALK databases which have patients with hallux valgus disease.
从足底压力分析人类步态对于人类健康至关重要。大多数研究工作都集中在对健康和不健康的足底模式进行分类上。与以往的工作不同，我们采用生成对抗网络为个体患者生成健康的足底压力图像。在这项工作中，我们没有用于训练的图像对，因此我们将问题转化为无监督生成对抗学习任务。我们的网络受益于多个组件：一个编码器-解码器生成器、一个基于卷积的判别器、一个基于卷积的评估网络，以及损失函数中的一个新项，以保留人的步态风格。我们的方法在患有拇外翻疾病的 CAD WALK 数据库上取得了很高的性能（99.8%）。
