Emotional states, as indicators of affect, are pivotal to overall health,
making their accurate prediction before onset crucial. Current studies are
primarily centered on immediate short-term affect detection using data from
wearable and mobile devices. These studies typically focus on objective sensory
measures, often neglecting other forms of self-reported information like
diaries and notes. In this paper, we propose a multimodal deep learning model
for affect status forecasting. This model combines a transformer encoder with a
pre-trained language model, facilitating the integrated analysis of objective
metrics and self-reported diaries. To validate our model, we conduct a
longitudinal study, enrolling college students and monitoring them over a year,
to collect an extensive dataset including physiological, environmental, sleep,
metabolic, and physical activity parameters, alongside open-ended textual
diaries provided by the participants. Our results demonstrate that the proposed
model achieves predictive accuracy of 82.50% for positive affect and 82.76% for
negative affect, a full week in advance. The effectiveness of our model is
further elevated by its explainability.

本研究提出一种多模态深度学习模型，结合了转换器编码器和预训练语言模型，利用客观指标和自我报告日记进行情感状态预测。通过进行为期一年的纵向研究，监测大学生参与者的身体、环境、睡眠、新陈代谢和体力活动参数，以及开放式的文本日记，实证结果表明该模型在提前一周预测积极和消极情感方面的准确率分别为 82.50% 和 82.76%。该模型的解释性进一步提高了其有效性。

整合可穿戴传感器数据和自报日记以进行个性化情绪预测

Integrating Wearable Sensor Data and Self-reported Diaries for  Personalized Affect Forecasting

Developing computational models for automatic affect prediction requires
valid self-reports about individuals' emotional interpretations of stimuli. In
this article, we highlight the important influence of the temporal distance
between a stimulus event and the moment when its experience is reported on the
provided information's validity. This influence stems from the time-dependent
and time-demanding nature of the involved cognitive processes. As such, reports
can be collected too late: forgetting is a widely acknowledged challenge for
accurate descriptions of past experience. For this reason, methods striving for
assessment as early as possible have become increasingly popular. However, here
we argue that collection may also occur too early: descriptions about very
recent stimuli might be collected before emotional processing has fully
converged. Based on these notions, we champion the existence of a temporal
distance for each type of stimulus that maximizes the validity of self-reports
-- a "right" time. Consequently, we recommend future research to (1)
consciously consider the potential influence of temporal distance on affective
self-reports when planning data collection, (2) document the temporal distance
of affective self-reports wherever possible as part of corpora for
computational modelling, and finally (3) and explore the effect of temporal
distance on self-reports across different types of stimuli.

本文研究影响情感自述有效性的时间距离问题，认为情感体验和自我报告之间的时间距离需要考虑，以获得最大的有效性，因此建议未来的研究在计划数据收集时有意识地考虑时间距离、记录情感报告的时间距离并探查不同类型的时间距离对自我报告的影响。

关于考虑评估情绪体验的 “正确” 时间距离：有效的自我报告既不迟也不早

A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience

Player modelling is the field of study associated with understanding players.
One pursuit in this field is affect prediction: the ability to predict how a
game will make a player feel. We present novel improvements to affect
prediction by using a deep convolutional neural network (CNN) to predict player
experience trained on game event logs in tandem with localized level structure
information. We test our approach on levels based on Super Mario Bros.
(Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario), as well
as original Super Mario Bros. levels. We outperform prior work, and demonstrate
the utility of training on player logs, even when lacking them at test time for
cross-domain player modelling.

本文提出了一种使用深度卷积神经网络来预测玩家体验的新方法，并通过游戏事件日志训练加上局部层次结构信息来实现情感预测的改进，试验结果表明我们的方法在预测玩家体验方面优于以往研究，并证明了在缺乏日志文件的情况下，通过训练进行跨领域玩家建模的实用性。