Future Image Prediction of Plantar Pressure During Gait Using Spatio-temporal Transformer.

Gait is one of the most frequently used forms of human movement during daily activities. The majority of works focus on exploring the dynamic factors during gait. Different from previous works, we adapt an image prediction task for anticipating the next frame in process of gait. In this work, we present a novel framework for human gait plantar pressure prediction using Spatio-temporal Transformer. We train the model to predict the next plantar pressure image in an image series while also learning frame feature encoders that predict the features of subsequent frames in the sequence. We proposed two new components in our loss function for considering temporality as well as smaller values in the image. Our model achieves superior results over several competitive baselines on the CAD WALK database. Clinical Relevance- This work can be used in robotic exoskeleton devices which are intelligent systems designed to improve gait performance and quality of life for the wearer that are being used to assist the recovery of walking ability for patients with disorders.

Unsupervised Generative Adversarial Network for Plantar Pressure Image-to-Image Translation.

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.