Computer Science > Robotics
arXiv:2309.09237 (cs)
[Submitted on 17 Sep 2023 (v1), last revised 12 Apr 2024 (this version, v3)]
Title:Human Movement Forecasting with Loose Clothing
View a PDF of the paper titled Human Movement Forecasting with Loose Clothing, by Tianchen Shen and 1 other authors
View PDFAbstract:Human motion prediction and trajectory forecasting are essential in human motion analysis. Nowadays, sensors can be seamlessly integrated into clothing using cutting-edge electronic textile (e-textile) technology, allowing long-term recording of human movements outside the laboratory. Motivated by the recent findings that clothing-attached sensors can achieve higher activity recognition accuracy than body-attached sensors. This work investigates the performance of human motion prediction using clothing-attached sensors compared with body-attached sensors. It reports experiments in which statistical models learnt from the movement of loose clothing are used to predict motion patterns of the body of robotically simulated and real human behaviours. Counterintuitively, the results show that fabric-attached sensors can have better motion prediction performance than rigid-attached sensors. Specifically, The fabric-attached sensor can improve the accuracy up to 40% and requires up to 80% less duration of the past trajectory to achieve high prediction accuracy (i.e., 95%) compared to the rigid-attached sensor.
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2309.09237 [cs.RO] |
| (orarXiv:2309.09237v3 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2309.09237 arXiv-issued DOI via DataCite |
Submission history
From: Tianchen Shen [view email][v1] Sun, 17 Sep 2023 10:56:06 UTC (4,489 KB)
[v2] Fri, 15 Mar 2024 13:56:23 UTC (4,217 KB)
[v3] Fri, 12 Apr 2024 11:48:19 UTC (4,217 KB)
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View a PDF of the paper titled Human Movement Forecasting with Loose Clothing, by Tianchen Shen and 1 other authors
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