[论文解读] Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data

Tanvir Islam, Peter Washington|arXiv (Cornell University)|Jul 7, 2023

Mental Health Research Topics被引用 8

一句话总结

论文提出了一个个性化的自监督学习框架，在个体多模态生物信号上进行预训练，以最少注释预测压力，在WESAD数据集上进行评估。

ABSTRACT

Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams.

研究动机与目标

• 解决来自多模态生物信号的压力预测中的标签稀缺性和数据异质性。
• 开发按用户进行个性化训练的模型，以捕捉个体基线动态。
• 利用自监督预训练从未标注数据中学习鲁棒表示。
• 将六种生物信号（EDA、ECG、EMG、TEMP、RESP、ACC）融合以改进压力预测。
• 在保持或提升性能的同时，展示对注释需求的减少。

提出的方法

• 将原始多模态生物信号数据分段为重叠窗口以作为模型输入。
• 使用面向主体的自监督预训练任务，从未标注数据中学习基线表示。
• 在下游压力预测任务上使用带标签的数据微调预训练模型。
• 通过晚期融合将六种模态融合，生成用于压力预测的多模态表示。
• 训练一个两阶段架构，冻结预训练层，使用任务特定的密集层。
• 使用RMSE进行评价，并比较SSL预训练与纯监督训练。

Figure 1: Personalized stress prediction framework involves segmenting the raw signal into overlapping data points and utilizing a self-supervised pre-training forecasting model (right model), which is then fine-tuned for stress prediction. We compare this model to a supervised learning model (left

实验结果

研究问题

• RQ1 在注释有限的情况下，逐用户自监督预训练是否能提升压力预测相较于非SSL的方法？
• RQ2 多模态个性化相较于单模态或非个性化模型，对性能有何影响？
• RQ3 SSL是否降低达到准确压力预测所需的标注样本量？

主要发现

• SSL预训练在所有受试者和注释类型下均显著降低RMSE，相较于纯监督模型。
• 即使仅用少量（如5个随机抽样数据点）的数据进行训练，SSL模型也优于非SSL模型。
• 个性化、多模态SSL在保持预测准确性的同时降低注释负担。
• 来自六种生物信号的融合多模态表示提升压力预测性能。
• 该方法在受试者特定基线的受控数据集（WESAD）上显示出稳健改进。

Figure 2: RMSE between a model fine-tuned after SSL and a model trained purely with supervised training for all subjects and all annotation types.