用多通道图像和注意力机制，从日常传感器数据中预测睡眠质量和压力水平。
MIS-LSTM: Multichannel Image-Sequence LSTM for Sleep Quality and Stress Prediction
- 将连续数据转为多通道图像，离散事件用专用1D-CNN编码，融合后输入LSTM建模长期依赖。
- 在ETRI Lifelog数据集上，宏平均F1达0.647，优于主流基线模型。
- 适合关注智能健康监测、可穿戴设备分析的研究者和开发者。
本文提出MIS-LSTM，一种融合CNN编码器与LSTM序列模型的混合框架，用于从多模态生活日志数据中逐日预测睡眠质量与压力水平。连续传感器流被划分为N小时区块并转换为多通道图像，稀疏离散事件则通过专用1D-CNN编码。卷积块注意力模块将两种模态融合为优化后的区块嵌入，再由LSTM聚合以捕捉长程时间依赖。为提升鲁棒性，引入不确定性感知集成方法UALRE，以高置信度个体预测替换低置信度多数投票。在2025 ETRI Lifelog Challenge数据集上的实验表明，基础MIS-LSTM取得宏平均F1 0.615；采用UALRE集成后提升至0.647，显著优于强基准模型LSTM、1D-CNN和CNN。消融实验验证：(i) 多通道优于堆叠垂直成像，(ii) 4小时区块粒度最优，(iii) 模态专用离散编码有效。
原文摘要 · Abstract (English)
This paper presents MIS-LSTM, a hybrid framework that joins CNN encoders with an LSTM sequence model for sleep quality and stress prediction at the day level from multimodal lifelog data. Continuous sensor streams are first partitioned into N-hour blocks and rendered as multi-channel images, while sparse discrete events are encoded with a dedicated 1D-CNN. A Convolutional Block Attention Module fuses the two modalities into refined block embeddings, which an LSTM then aggregates to capture long-range temporal dependencies. To further boost robustness, we introduce UALRE, an uncertainty-aware ensemble that overrides lowconfidence majority votes with high-confidence individual predictions. Experiments on the 2025 ETRI Lifelog Challenge dataset show that Our base MISLSTM achieves Macro-F1 0.615; with the UALRE ensemble, the score improves to 0.647, outperforming strong LSTM, 1D-CNN, and CNN baselines. Ablations confirm (i) the superiority of multi-channel over stacked-vertical imaging, (ii) the benefit of a 4-hour block granularity, and (iii) the efficacy of modality-specific discrete encoding.
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