2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)最新文献_第8页

Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors 基于fnirs测量的脑信号的疼痛检测:基于小波变换和Dirichlet过程先验贝叶斯分层建模的个性化机器学习方法

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-07-30 DOI: 10.1109/ACIIW.2019.8925076

D. Martinez, Ke Peng, Arielle J. Lee, D. Borsook, Rosalind W. Picard

{"title":"Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors","authors":"D. Martinez, Ke Peng, Arielle J. Lee, D. Borsook, Rosalind W. Picard","doi":"10.1109/ACIIW.2019.8925076","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925076","url":null,"abstract":"Currently self-report pain ratings are the gold standard in clinical pain assessment. However, the development of objective automatic measures of pain could substantially aid pain diagnosis and therapy. Recent neuroimaging studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for pain detection. This is a brain-imaging technique that provides non-invasive, long-term measurements of cortical hemoglobin concentration changes. In this study, we focused on fNIRS signals acquired exclusively from the prefrontal cortex, which can be accessed unobtrusively, and derived an algorithm for the detection of the presence of pain using Bayesian hierarchical modelling with wavelet features. This approach allows personalization of the inference process by accounting for inter-participant variability in pain responses. Our work highlights the importance of adopting a personalized approach and supports the use of fNIRS for pain assessment.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Speech, Head, and Eye-based Cues for Continuous Affect Prediction 基于语言、头部和眼睛的连续影响预测线索

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-07-23 DOI: 10.1109/ACIIW.2019.8925042

J. O'Dwyer

{"title":"Speech, Head, and Eye-based Cues for Continuous Affect Prediction","authors":"J. O'Dwyer","doi":"10.1109/ACIIW.2019.8925042","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925042","url":null,"abstract":"Continuous affect prediction involves the discrete time-continuous regression of affect dimensions. Researchers in this domain are currently embracing multimodal model input. This provides motivation for researchers to investigate previously unexplored affective cues. Speech-based cues have traditionally received the most attention for affect prediction, however, nonverbal inputs have significant potential to increase the performance of affective computing systems and enable affect modelling in the absence of speech. Non-verbal inputs that have received little attention for continuous affect prediction include head and eye-based cues. Both head and eye-based cues are involved in emotion displays and perception. Additionally, these cues can be estimated non-intrusively from video, using computer vision tools. This work exploits this gap by comprehensively investigating head and eye-based features and their combination with speech for continuous affect prediction. Hand-crafted, automatically generated and convolutional neural network (CNN) learned features from these modalities will be investigated for continuous affect prediction. The highest performing feature set combinations will answer how effective these features are for the prediction of an individual's affective state.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121637365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning 基于生理信号和多视角多任务机器学习的真实驾驶诱导情感状态检测

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-07-19 DOI: 10.1109/ACIIW.2019.8925190

D. Martinez, Neska El Haouij, Rosalind W. Picard

{"title":"Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning","authors":"D. Martinez, Neska El Haouij, Rosalind W. Picard","doi":"10.1109/ACIIW.2019.8925190","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925190","url":null,"abstract":"Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and increase safety, comfort and well-being. Recent advances in affective computing have enabled the detection of such states. This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety. In this work, we propose a multiview multi-task machine learning method for the detection of driver's affective states using physiological signals. The proposed approach is able to account for inter-drive variability in physiological responses while enabling interpretability of the learned models, a factor that is especially important in systems deployed in the real world. We evaluate the models on three different datasets containing real-world driving experiences. Our results indicate that accounting for drive-specific differences significantly improves model performance.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128727648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Towards automatic estimation of conversation floors within F-formations 朝着自动估计f -队形内的对话楼层的方向发展

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-07-19 DOI: 10.1109/ACIIW.2019.8925065

Chirag Raman, H. Hung

{"title":"Towards automatic estimation of conversation floors within F-formations","authors":"Chirag Raman, H. Hung","doi":"10.1109/ACIIW.2019.8925065","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925065","url":null,"abstract":"The detection of free-standing conversing groups has received significant attention in recent years. In the absence of a formal definition, most studies operationalize the notion of a conversation group either through a spatial or a temporal lens. Spatially, the most commonly used representation is the F-formation, defined by social scientists as the configuration in which people arrange themselves to sustain an interaction. However, the use of this representation is often accompanied with the simplifying assumption that a single conversation occurs within an F-formation. Temporally, various categories have been used to organize conversational units; these include, among others, turn, topic, and floor. Some of these concepts are hard to define objectively by themselves. The present work constitutes an initial exploration into unifying these perspectives by primarily posing the question: can we use the observation of simultaneous speaker turns to infer whether multiple conversation floors exist within an F-formation? We motivate a metric for the existence of distinct conversation floors based on simultaneous speaker turns, and provide an analysis using this metric to characterize conversations across F-formations of varying cardinality. We contribute two key findings: firstly, at the average speaking turn duration of about two seconds for humans, there is evidence for the existence of multiple floors within an F-formation; and secondly, an increase in the cardinality of an F-formation correlates with a decrease in duration of simultaneous speaking turns.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124304118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech - a Deep Learning approach 一种控制合成语音中情感表达的方法——一种深度学习方法

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-07-05 DOI: 10.1109/ACIIW.2019.8925241

Noé Tits

引用次数: 10