用频时融合特征检测床上人员，准确率超95%
Spectral-Temporal Fusion Representation for Person-in-Bed Detection
- 结合频域与时域特征，提升信号表征能力
- 分段检测准确率达100%，流式检测达95.55%
- 适合智能健康监测与可穿戴设备应用
本研究基于ICASSP 2025信号处理大赛的基于加速度计的床上人员检测挑战赛，旨在通过加速度信号判断床铺占用状态。任务分为分段检测（'在床'与'不在床'）和流式检测两个赛道，面临个体差异、体位变化及外部干扰等挑战。本文提出一种基于频时融合的特征表示方法，并结合Mixup数据增强，采用交并比（IoU）损失优化检测精度。在两个赛道中，方法分别取得100.00%和95.55%的检测得分，分别获得第一名和第三名。
原文摘要 · Abstract (English)
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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