  Cooking is a central activity of daily living, supporting independence as
well as mental and physical health. However, prior work has highlighted key
barriers for people with low vision (LV) to cook, particularly around safely
interacting with tools, such as sharp knives or hot pans. Drawing on recent
advancements in computer vision (CV), we present CookAR, a head-mounted AR
system with real-time object affordance augmentations to support safe and
efficient interactions with kitchen tools. To design and implement CookAR, we
collected and annotated the first egocentric dataset of kitchen tool
affordances, fine-tuned an affordance segmentation model, and developed an AR
system with a stereo camera to generate visual augmentations. To validate
CookAR, we conducted a technical evaluation of our fine-tuned model as well as
a qualitative lab study with 10 LV participants for suitable augmentation
design. Our technical evaluation demonstrates that our model outperforms the
baseline on our tool affordance dataset, while our user study indicates a
preference for affordance augmentations over the traditional whole object
augmentations.
