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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.10818 |
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| _version_ | 1866915339698176000 |
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| author | Valkov, Dimitar Kockwelp, Pascal Daiber, Florian Krüger, Antonio |
| author_facet | Valkov, Dimitar Kockwelp, Pascal Daiber, Florian Krüger, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grasp Prediction based on Local Finger Motion Dynamics Valkov, Dimitar Kockwelp, Pascal Daiber, Florian Krüger, Antonio Human-Computer Interaction H.5.2 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. |
| title | Grasp Prediction based on Local Finger Motion Dynamics |
| topic | Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2506.10818 |