用前一晚睡眠数据提升可穿戴设备情绪识别效果
NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-series
- 用轻量提示调优融合睡眠数据，不改动主模型
- 在心电、光电、皮电信号上均显著提效
- 适合关注心理健康监测的可穿戴研究者
睡眠是情绪调节与心理健康的關鍵因素。本研究探索将前一晚睡眠指标融入可穿戴设备的情绪识别中。为此，提出NapTune框架，通过在冻结的预训练可穿戴时序编码器各Transformer层添加轻量级提示参数，将睡眠指标作为额外输入进行训练。实证评估表明，引入睡眠数据后，无论在心电（ECG）、光电容积脉搏波（PPG）还是皮肤电活动（EDA）等多类可穿戴信号上，情绪识别性能均显著提升，且样本效率更高。该方法优于现有最佳基线与单模态变体。进一步分析显示，不同睡眠指标对各类情绪识别的影响各异。
原文摘要 · Abstract (English)
Sleep is known to be a key factor in emotional regulation and overall mental health. In this study, we explore the integration of sleep measures from the previous night into wearable-based mood recognition. To this end, we propose NapTune, a novel prompt-tuning framework that utilizes sleep-related measures as additional inputs to a frozen pre-trained wearable time-series encoder by adding and training lightweight prompt parameters to each Transformer layer. Through rigorous empirical evaluation, we demonstrate that the inclusion of sleep data using NapTune not only improves mood recognition performance across different wearable time-series namely ECG, PPG, and EDA, but also makes it more sample-efficient. Our method demonstrates significant improvements over the best baselines and unimodal variants. Furthermore, we analyze the impact of adding sleep-related measures on recognizing different moods as well as the influence of individual sleep-related measures.
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