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arXiv · 2412.19404

Spectral-Temporal Fusion Representation for Person-in-Bed Detection

Xuefeng Yang·Shiheng Zhang·Jian Guan·Feiyang Xiao·Wei Lu·Qiaoxi Zhu
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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.

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Xuefeng Yang, Shiheng Zhang, Jian Guan, Feiyang Xiao, Wei Lu, Qiaoxi Zhu. 2024-12-27. Spectral-Temporal Fusion Representation for Person-in-Bed Detection. https://arxiv.org/abs/2412.19404

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