The ability to predict the object the user intends to grasp offers essential contextual information and may help to leverage the effects of point-to-point latency in interactive environments. This paper explores the feasibility and accuracy of real-time recognition of uninstrumented objects based on hand kinematics during reach-to-grasp actions. In a data collection study, we recorded the hand motions of 16 participants while reaching out to grasp and then moving real and synthetic objects. Our results demonstrate that even a simple LSTM network can predict the time point at which the user grasps an object with a precision better than 21 ms and the current distance to this object with a precision better than 1 cm. The target's size can be determined in advance with an accuracy better than 97%. Our results have implications for designing adaptive and fine-grained interactive user interfaces in ubiquitous and mixed-reality environments.
@article{arxiv.2506.10818,
title = {Grasp Prediction based on Local Finger Motion Dynamics},
author = {Dimitar Valkov and Pascal Kockwelp and Florian Daiber and Antonio Krüger},
journal= {arXiv preprint arXiv:2506.10818},
year = {2025}
}