IEEE Transactions on Affective ComputingIEEE情感计算汇刊
IEEE Transactions on Affective Computing(英文缩写 IEEE T AFFECT COMPUT),ISSN 1949-3045,eISSN 1949-3045,中文译名:IEEE情感计算汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 13.990 | Q1 |
| 2022 | 11.200 | Q1 |
| 2023 | 9.600 | Q1 |
| 2024 | 9.800 | Q1 |
| 2025 | 11.300 | Q1 |
IEEE Transactions on Affective Computing 最新收录文献
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1. ECG-based Emotion Prediction in Real Life: Tackling Heterogeneity Through a Hybrid Personalization Method.
1. 基于心电图的实际生活中的情绪预测：通过混合个性化方法应对异质性PMID:日期:2026-06-11Predicting people's momentary emotions in real life remains an underexplored challenge in affective computing. Wearable electrocardiography (ECG) offers a promising tool for this purpose, given its psychological relevance to emotion and increasing affordability in recent years. However, a major obstacle lies in the substantial individual differences in the associations between emotional experiences and physiological responses, which is further amplified in real-world settings. Emerging personalization methods offer a potential solution, but their utility for real-life ECG-based emotion prediction has yet to be evaluated. To address this, the present study leverages the largest real-life ECG dataset to date (N = 363) to evaluate a hybrid personalization approach that tailors model training using a subset of the most informative participants. We compared three modeling strategies-idiographic personalization, hybrid personalization, and a general model-across binary classification tasks for positive affect (PA) and negative affect (NA). The hybrid personalization method outperformed both the idiographic personalization (PA: 76.6% vs. 71.5%; NA: 77.7% vs. 74.3%) and the general model (PA: 55.5%; NA: 49.3%), with the largest advantages observed under limited data conditions. Follow-up analyses revealed that data from individuals with higher emotional awareness contributed more to model performance. These findings highlight the utility of hybrid personalization for real-world ECG-based emotion prediction and suggest that emotional awareness may serve as a useful trait for identifying informative training data and enabling more efficient model development.
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2. Strength in Numbers, Power in Subjectivity: Scalable Modeling of Individual Annotators for Emotion Recognition Within and Across Corpora.
2. 以数量铸优势，以主体性聚力量：面向语料库内与跨语料库情感识别的可扩展个体标注者建模PMID:日期:2026-04-23Emotion expression and perception are nuanced, complex, and highly subjective processes. As a result, when multiple annotators label emotional data, the resulting labels generally contain high degrees of variability. This variability is often addressed by averaging annotator labels into a single averaged or majority-voted ground-truth label. However, an averaged label may mask variation among the perception of individuals, which is at the core of subjective perception. This is an issue because inter-annotator variability contains valuable information about both the sample being evaluated and the annotators themselves. Previous works have proposed methods to retain this variability by predicting distributions of annotator perception, rather than a single label. However, distributions lose the information about the individual annotators. Other approaches have instead learned models of individual annotators. Yet, computational complexity limited these approaches to considering only small sets of annotators. In this paper, we present a new approach that learns to efficiently model arbitrarily large numbers of individual annotators. We demonstrate the efficacy of this approach, compared to previous state-of-the-art approaches, and the insights that can be gleaned once annotator variability is retained. For the first time, it is possible to ask when it is beneficial to model every single annotator, as an individual, vs. when it is beneficial instead to model a more limited number of annotators who have provided ample data.
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3. Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment.
3. 描述你所在的位置：利用环境文本描述提升语音情感识别的噪声鲁棒性PMID:日期:2025-11-06(SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER performance under noisy conditions. To address this task, we propose a text-guided, environment-aware training where an SER model is trained with contaminated speech samples and their paired noise description. We use a pre-trained text encoder to extract the text-based environment embedding and then fuse it to a transformer-based SER model during training and inference. We demonstrate the effectiveness of our approach through our experiment with the MSP-Podcast corpus and real-world additive noise samples collected from the Freesound and DEMAND repository. Our experiment indicates that the text-based environment descriptions processed by a (LLM) produce representations that improve the noise-robustness of the SER system. With a (CL)-based representation, our proposed method can be improved by jointly fine-tuning the text encoder with the emotion recognition model. Under the -5dB (SNR) level, fine-tuning the text encoder improves our CL-based representation method by 76.4% (arousal), 100.0% (dominance), and 27.7% (valence).
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4. Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.
PMID:日期:2025-05-09As mobile sensing research for depression assessment is rapidly expanding, it is vital to uncover fundamental patterns and overarching limitations in the datasets available for such research, especially as many analyses are conducted on the same datasets. As such, we conduct a scoping review by identifying 80 datasets through the start of 2024 that contain smartphone modalities and depression labels. These datasets originated from 72 manuscripts and approximately 60 research groups. The most collected smartphone modalities included location and/or activity for 68.75%, communication logs for 47.5%, phone use for 41.25%, vocal utterances for 30%, and WiFi and/or Bluetooth connectivity for 28.75% of the datasets. The PHQ-8 and PHQ-9 (Patient Health Questionnaire) were the most popular screening instruments, administered for 53.75% of datasets. Of the 80 datasets, 31.25% recruited from student populations, 22.5% from patient populations, and 7.5% recruited crowdsourced workers. Excluding the nine datasets that did not report demographics, 73% reported a majority of women participants. Given the importance of datasets on analysis outcomes, this scoping review is an invaluable resource to inform the state of science and guide future mobile health research.
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5. Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom.
5. 基于可穿戴传感器社交焦虑个体在Zoom社交情境中的多模态生理反应PMID:日期:2025-04-21Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e., alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study ( = 46) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (≥34 out of 80). Univariate paired difference tests, multivariate random forest models, and cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, and that there is considerable variability in physiological response patterns even among distinguishable contexts. Implications for real-world context detection and future deployment of JITAIs are discussed.
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6. Mechanoreceptive Aβ primary afferents discriminate naturalistic social touch inputs at a functionally relevant time scale.
6. 机械感受性Aβ初级传入神经在功能相关的时间尺度上区分自然主义社交触觉输入PMID:日期:2024-07-29Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered social touch expressions. Leveraging statistical and classification analyses, we found that single units of multiple mechanoreceptive Aβ subtypes, especially slowly adapting type II (SA-II) and fast adapting hair follicle afferents (HFA), can reliably differentiate social touch expressions at accuracies similar to human recognition. We then identified the most informative firing patterns of SA-II and HFA afferents, which indicate that average durations of 3-4 s of firing provide sufficient discriminative information. Those two subtypes also exhibit robust tolerance to spike-timing shifts of up to 10-20 ms, varying with touch expressions due to their specific firing properties. Greater shifts in spike-timing, however, can change a firing pattern's envelope to resemble that of another expression and drastically compromise an afferent's discrimination capacity. Altogether, the findings indicate that SA-II and HFA afferents differentiate the skin contact of social touch at time scales relevant for such interactions, which are 1-2 orders of magnitude longer than those for non-social touch.
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7. Emotion Recognition in the Real-World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder.
7. 现实世界中的情绪识别：从双相情感障碍个体的自然语音数据中被动收集和估计情绪PMID:日期:2024-05-30Emotions provide critical information regarding a person's health and well-being. Therefore, the ability to track emotion and patterns in emotion over time could provide new opportunities in measuring health longitudinally. This is of particular importance for individuals with bipolar disorder (BD), where emotion dysregulation is a hallmark symptom of increasing mood severity. However, measuring emotions typically requires self-assessment, a willful action outside of one's daily routine. In this paper, we describe a novel approach for collecting real-world natural speech data from daily life and measuring emotions from these data. The approach combines a novel data collection pipeline and validated robust emotion recognition models. We describe a deployment of this pipeline that included parallel clinical and self-report measures of mood and self-reported measures of emotion. Finally, we present approaches to estimate clinical and self-reported mood measures using a combination of passive and self-reported emotion measures. The results demonstrate that both passive and self-reported measures of emotion contribute to our ability to accurately estimate mood symptom severity for individuals with BD.
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8. Multimodal Prediction of Obsessive-Compulsive Disorder and Comorbid Depression Severity and Energy Delivered by Deep Brain Electrodes.
8. 深部脑电极传递能量对强迫症及共病抑郁严重程度的多模态预测PMID:日期:2024-04-30To develop reliable, valid, and efficient measures of obsessive-compulsive disorder (OCD) severity, comorbid depression severity, and total electrical energy delivered (TEED) by deep brain stimulation (DBS), we trained and compared random forests regression models in a clinical trial of participants receiving DBS for refractory OCD. Six participants were recorded during open-ended interviews at pre- and post-surgery baselines and then at 3-month intervals following DBS activation. Ground-truth severity was assessed by clinical interview and self-report. Visual and auditory modalities included facial action units, head and facial landmarks, speech behavior and content, and voice acoustics. Mixed-effects random forest regression with Shapley feature reduction strongly predicted severity of OCD, comorbid depression, and total electrical energy delivered by the DBS electrodes (intraclass correlation, ICC, = 0.83, 0.87, and 0.81, respectively. When random effects were omitted from the regression, predictive power decreased to moderate for severity of OCD and comorbid depression and remained comparable for total electrical energy delivered (ICC = 0.60, 0.68, and 0.83, respectively). Multimodal measures of behavior outperformed ones from single modalities. Feature selection achieved large decreases in features and corresponding increases in prediction. The approach could contribute to closed-loop DBS that would automatically titrate DBS based on affect measures.
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9. A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress.
9. 压力检测的新视角：一种检测良性压力和不良压力的自动化方法PMID:日期:2023-10-16Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace.
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10. Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring.
10. 用于检测母婴二元互动中母亲抑郁的多模态特征选择PMID:日期:2023-02-16Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathology within families. To contribute to our understanding of depression within families and to inform modality selection and feature reduction, it is critical to identify interpretable features in developmentally appropriate contexts. Mothers with and without depression were studied. Depression was defined as history of treatment for depression and elevations in current or recent symptoms. We explored two multimodal feature selection strategies in dyadic interaction tasks of mothers with their adolescent children for depression detection. Modalities included face and head dynamics, facial action units, speech-related behavior, and verbal features. The initial feature space was vast and inter-correlated (collinear). To reduce dimensionality and gain insight into the relative contribution of each modality and feature, we explored feature selection strategies using Variance Inflation Factor (VIF) and Shapley values. On an average collinearity correction through VIF resulted in about 4 times feature reduction across unimodal and multimodal features. Collinearity correction was also found to be an optimal intermediate step prior to Shapley analysis. Shapley feature selection following VIF yielded best performance. The top 15 features obtained through Shapley achieved 78% accuracy. The most informative features came from all four modalities sampled, which supports the importance of multimodal feature selection.