A dataset is provided with
6,369 gestures of 40 distinct types,
collected from
20 participants using a custom-made
index-Finger Augmentation Device (iFAD). The dataset represents a companion resource
for
(Vatavu, 2023), where iFAD gestures were examined in detail
with a dedicated taxonomy and a user study addressing various aspects of user performance and perception.
The dataset includes gestures of diverse complexity, from simple taps
on the iFAD to specific finger and hand poses, stroke-gesture articulations
of geometrical shapes, letters, and symbols in mid-air, and body-referenced
gestures. A few examples are provided below: