一个人的时间知觉（POTP）与他们的精神状态和压力反应密切相关，因此可以提供一种易于量化的持续心理健康监测手段。在这项工作中，我们开发了一个自定义实验和机器学习（ML）模型，用于从可穿戴生物传感器获得的生物标志物中预测POTP。我们首先确认，个体在恐惧或悲伤时经历的时间流逝比平时慢（p = 0.046），而在认知任务中经历的时间流逝比平时快（p = 2 x 10^-5）。然后，我们将与快速，慢速和正常POTP相关的实验片段分组在一起，并训练ML模型，以根据人的生物标志物在这些状态之间进行分类。分类器的加权平均F-1得分为79%，快速通过时间类的F-1得分最高，为93%。接下来，我们对每个人的POTP进行分类，而不管手头的任务如何，当区分时间过得比平时快而不是慢时，达到了77.1%的F-1分数。在这两个分类器中，来自呼吸、心电图、皮肤电导和皮肤温度信号的生物标志物对分类器输出的贡献最大，从而使得能够使用非侵入性、可穿戴生物传感器进行实时POTP监测。
英文摘要：
A person's passage of time perception (POTP) is strongly linked to their mental state and stress response, and can therefore provide an easily quantifiable means of continuous mental health monitoring. In this work, we develop a custom experiment and Machine Learning (ML) models for predicting POTP from biomarkers acquired from wearable biosensors. We first confirm that individuals experience time passing slower than usual during fear or sadness (p = 0.046) and faster than usual during cognitive tasks (p = 2 x 10^-5). Then, we group together the experimental segments associated with fast, slow, and normal POTP, and train a ML model to classify between these states based on a person's biomarkers. The classifier had a weighted average F-1 score of 79%, with the fast-passing time class having the highest F-1 score of 93%. Next, we classify each individual's POTP regardless of the task at hand, achieving an F-1 score of 77.1% when distinguishing time passing faster rather than slower than usual. In the two classifiers, biomarkers derived from the respiration, electrocardiogram, skin conductance, and skin temperature signals contributed most to the classifier output, thus enabling real-time POTP monitoring using noninvasive, wearable biosensors.
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors
Aston University（阿斯顿大学）；University of Toronto（多伦多大学）；University Health Network（大学健康网络）；Florida Institute of Technology（佛罗里达理工学院）；Hai Duong Central College of Pharmacy（海洞中央药学院）；Stanford University（斯坦福大学）；Industrial University of Ho Chi Minh City（胡志明市工业大学）；The University of Alabama at Birmingham（伯明翰大学）；Pukyong National University（浦项国立大学）
Decoding Emotional Valence from Wearables: Can Our Data Reveal Our True Feelings?
Sano Centre for Computational Medicine（萨诺计算医学中心）；Poznan University of Technology（波兹南理工大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals
Halıcıoğlu Data Science Institute, University of California San Diego（哈利奇奥格鲁数据科学研究所，加州大学圣地亚哥分校）；Schools of Engineering, Arizona State University（工程学院，亚利桑那州立大学）
Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data
Engineering and Technology Institute Groningen, University of Groningen（Groningen大学工程与技术学院）；Department of Human Movement Sciences, University of Groningen, University Medical Center Groningen（Groningen大学人类运动科学系）
SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks
Rice University（莱斯大学）；Massachusetts General Hospital, Harvard Medical School（马萨诸塞总医院，哈佛医学院）；Oregon Health & Science University（俄勒冈健康与科学大学）；Monash University（莫纳什大学）
RNNs on Monitoring Physical Activity Energy Expenditure in Older People
Leiden Institute of Advanced Computer Science, Universiteit Leiden（莱顿大学莱顿高级计算机科学研究所）；Molecular Epidemiology, Dept. Biomedical Data Science, LUMC, Leiden（莱顿大学医学中心生物医学数据科学系分子流行病学）；Data Science research group, University of Twente（特文特大学数据科学研究组）
