A high-resolution and whole-body dataset of hand-object contact areas based on 3D scanning method

Hand contact data, reflecting the intricate behaviours of human hands
during object operation, exhibits significant potential for analysing hand
operation patterns to guide the design of hand-related sensors and robots,
and predicting object properties. However, these potential applications
are hindered by the constraints of low resolution and incomplete capture
of the hand contact data. Leveraging a non-contact and high-precision 3D
scanning method for surface capture, a high-resolution and whole-body hand
contact dataset, named as Ti3D-contact, is constructed in this work. The
dataset, with an average resolution of 0.72 mm, contains 1872 sets of
texture images and 3D models. The contact area during hand operation is
whole-body painted on gloves, which are captured as the high-resolution
original hand contact data through a 3D scanner. Reliability validation on
Ti3D-contact is conducted and hand movement classification with 95%
precision is achieved using the acquired hand contact dataset. The
properties of high-resolution and whole-body capturing make the acquired
dataset exhibit a promising potential application in hand posture
recognition and hand movement prediction.