A method of state recognition of dressing clothes based on dynamic state matching
Abstract
This paper describes a method of clothing recognition using dynamic state matching. One purpose of this research is to develop a function that is used to observe the progress of dressing a bottom at a toilet or a bedside. In this dressing, although we just have to do is to put my or others' legs on the bottom. some undesirable situations may occur. For instance, the feet gets hung up on the part of the bottom, and so on. Assuming to use a single camera, we propose a method that distinguishes whether or not a behavior of dressing a bottom is working in order. The input data is an image stream that captures a dressing sequence. Optical flow is calculated from two consecutive images, and then the distribution of the flows is used to distinguish the present clothing state by matching the optical flow with training dataset. The method outputs whether or not the present clothing is in a successful situation. The effectiveness of the proposed method was proven by means of real image data.
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The system needs to detect success and different error states.
“In addition, the system needs to detect success and different error states [298,301,309,315,316,317].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [315]
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Probabilistic real-time user posture tracking for personalized robot-assisted dressing
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Bayesian nonparametric learning of cloth models for real-time state estimation
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Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions
Yamazaki, K., Oya, R., Nagahama, K., Okada, K., & Inaba, M. (2014) · 2014 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2014.7028101 · Cited by 42 · see citing works
Self-citationAn assistive robot to support dressing - strategies for planning and error handling
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Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“In addition, the system needs to detect success and different error states [298,301,309,315–317].”
Background
- Authors
- Yamazaki, K., Oya, R., Nagahama, K., & Inaba, M.
- Affiliations
- Shinshu University (JP)
- The University of Tokyo (JP)
- Venue
- Proceedings of the 2013 IEEE/SICE International Symposium on System Integration
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 406-411
- OpenAlex
- W1997291854
- Cited by
- 20
- References
- 14
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2020: 1 · 2019: 2 · 2018: 5 · 2017: 3 · 2016: 1 · 2015: 1 · 2014: 2
- FWCI
- 0.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/SICE International Symposium on System Integration (SII) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- dressing assistance, state recognition, optical flow, single-camera video, image streams, elderly care
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