  We present a knowledge augmentation strategy for assessing the diagnostic
groups and gait impairment from monocular gait videos. Based on a large-scale
pre-trained Vision Language Model (VLM), our model learns and improves visual,
textual, and numerical representations of patient gait videos, through a
collective learning across three distinct modalities: gait videos,
class-specific descriptions, and numerical gait parameters. Our specific
contributions are two-fold: First, we adopt a knowledge-aware prompt tuning
strategy to utilize the class-specific medical description in guiding the text
prompt learning. Second, we integrate the paired gait parameters in the form of
numerical texts to enhance the numeracy of the textual representation. Results
demonstrate that our model not only significantly outperforms state-of-the-art
(SOTA) in video-based classification tasks but also adeptly decodes the learned
class-specific text features into natural language descriptions using the
vocabulary of quantitative gait parameters. The code and the model will be made
available at our project page.
