Spectral-Temporal Fusion Representation for Person-in-Bed Detection
Xuefeng Yang, Shiheng Zhang, Jian Guan, Feiyang Xiao, Wei Lu, Qiaoxi Zhu
Abstract
This study is based on the ICASSP 2025 Signal Processing Grand Challenge’s Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection and streaming detection, facing challenges such as individual differences, posture variations, and external disturbances. We propose a spectral-temporal fusion-based feature representation method with mixup data augmentation, and adopt Intersection over Union (IoU) loss to optimize detection accuracy. In the two tracks, our method achieved outstanding results of 100.00% and 95.55% in detection scores, securing first place and third place, respectively.
BibTeX
@inproceedings{icassp2025_spectraltemporal,
title = {Spectral-Temporal Fusion Representation for Person-in-Bed Detection},
author = {Xuefeng Yang and Shiheng Zhang and Jian Guan and Feiyang Xiao and Wei Lu and Qiaoxi Zhu},
booktitle = {ICASSP 2025},
year = {2025}
}