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.

翻译：本研究基于ICASSP 2025信号处理大挑战中的"基于加速度计的床上人员检测挑战"，旨在利用加速度计信号判断床位占用情况。该任务分为两个赛道："在床"与"离床"的片段化检测，以及流式检测，面临个体差异、姿态变化和外部干扰等挑战。我们提出了一种基于频谱-时序融合的特征表征方法，结合混合数据增强技术，并采用交并比损失函数优化检测精度。在两个赛道中，我们的方法分别取得了100.00%和95.55%的优异检测分数，分别获得第一名和第三名。