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arXiv · 2506.10818

Grasp Prediction based on Local Finger Motion Dynamics

Dimitar Valkov·Pascal Kockwelp·Florian Daiber·Antonio Krüger
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Abstract

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.

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Dimitar Valkov, Pascal Kockwelp, Florian Daiber, Antonio Krüger. 2025-06-12. Grasp Prediction based on Local Finger Motion Dynamics. https://arxiv.org/abs/2506.10818

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