Summary of Spectral-temporal Fusion Representation For Person-in-bed Detection, by Xuefeng Yang et al.
Spectral-Temporal Fusion Representation for Person-in-Bed Detection
by Xuefeng Yang, Shiheng Zhang, Jian Guan, Feiyang Xiao, Wei Lu, Qiaoxi Zhu
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a spectral-temporal fusion-based feature representation method for accelerometer-based person-in-bed detection, which outperformed existing methods in the ICASSP 2025 Signal Processing Grand Challenge’s Accelerometer-Based Person-in-Bed Detection Challenge. The task involves detecting whether a person is in bed or not using accelerometer signals, which is challenging due to individual differences, posture variations, and external disturbances. To address these challenges, the proposed method uses mixup data augmentation and IoU loss to optimize detection accuracy. The results show outstanding performance with detection scores of 100.00% and 95.55% in the two tracks, securing first place and third place respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special sensors called accelerometers to tell if someone is lying in bed or not. This is a big challenge because people can move around, change position, or even get out of bed, which makes it hard for machines to detect. The researchers came up with a new way to analyze the data from these sensors, which helps them figure out what’s happening. They tested this method and got really good results, beating other teams in a competition. |
Keywords
» Artificial intelligence » Data augmentation » Signal processing
