2025/06/12 by Dimitar Valkov, Pascal Kockwelp, Valkov, Dimitar +5
Computer Science · Engineering · Neuroscience · #FOS: Computer and information sciences #H.5.2 #Hand Gesture Recognition Systems #Human-Computer Interaction (cs.HC) #Motor Control and Adaptation #Robot Manipulation and Learning
paper · pdf · doi:10.48550/arxiv.2506.10818
openalex publication_date 2025/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.