2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)最新文献_第3页

Learning Temporal and Bodily Attention in Protective Movement Behavior Detection 保护动作行为检测中的时间注意和身体注意学习

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925084

Chongyang Wang, Min Peng, Temitayo A. Olugbade, N. Lane, A. Williams, N. Bianchi-Berthouze

{"title":"Learning Temporal and Bodily Attention in Protective Movement Behavior Detection","authors":"Chongyang Wang, Min Peng, Temitayo A. Olugbade, N. Lane, A. Williams, N. Bianchi-Berthouze","doi":"10.1109/ACIIW.2019.8925084","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925084","url":null,"abstract":"For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183007","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: 19

Enriching Discrete Actions with Impactful Emotions 用有影响力的情绪丰富离散的行动

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925107

R. Rodrigues

引用次数: 0

Wearable Sensing Technology for Capturing and Sharing Emotional Experience of Running 捕捉和分享跑步情感体验的可穿戴传感技术

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925104

Tao Bi

引用次数: 2

A stress recognition system using HRV parameters and machine learning techniques 使用HRV参数和机器学习技术的应力识别系统

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925142

Giorgos Giannakakis, K. Marias, M. Tsiknakis

引用次数: 30

A Sensor-based Framework for Real-time Detection and Alleviation of Public Speaking Anxiety 基于传感器的演讲焦虑实时检测与缓解框架

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925262

Everlyne Kimani

引用次数: 3

Feedback of Physiological-Based Emotion before Publishing Emotional Expression on Social Media 在社交媒体上发表情感表达前的生理情感反馈

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925097

Feng Chen, Peeraya Sripian, Midori Sugaya

{"title":"Feedback of Physiological-Based Emotion before Publishing Emotional Expression on Social Media","authors":"Feng Chen, Peeraya Sripian, Midori Sugaya","doi":"10.1109/ACIIW.2019.8925097","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925097","url":null,"abstract":"Making emotional expressions on social media has recently become an ordinary part of life, but sometimes people might send messages with the wrong expression to other people through these media based on unconscious emotions such as anger. However, it is often difficult to recognize these unconscious emotions, and easy to send inappropriate expressions to other people without proper consideration. This could cause an unpleasant experience. To avoid these situations, it is expected that some observable mechanism could detect and communicate the unconscious emotions to the user before they send the message. These days, there are approaches that can detect unconscious emotions using physiological sensors such as EEGs and heartbeat sensors. These approaches provide the procedure to make unconscious emotions observable and communicated to the user in real-time. We apply this technology for detecting the mismatch between the unconscious emotion and expression before sending the message. Based on this idea, we design and implement the mechanism for detecting the mismatch and feed it back to the user of social media. We carry out an experiment using the proposed system. The preliminary result shows that the system tends to be effective for the purpose.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126065135","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: 1

Multi-Modal Depression Detection and Estimation 多模态凹陷检测与估计

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925288

Le Yang

{"title":"Multi-Modal Depression Detection and Estimation","authors":"Le Yang","doi":"10.1109/ACIIW.2019.8925288","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925288","url":null,"abstract":"Depression and anxiety disorders are critical problems in modern society. The WHO studies suggest that roughly 12.8 percent of the world's population are suffering from a depressive disorder. In this work, we propose several novel approaches towards multi-modal depression detection and estimation. Our previous studies mainly explored the multi-modal features and multi-modal fusion strategies, experimental results showed that the proposed hybrid depression classification and estimation multi-modal fusion framework obtains promising performance. The current work contains two parts: 1) In order to mitigate the impact of lack of data on training depression deep models, we utilize Generative Adversarial Network (GAN) to augment depression audio features, so as to improve depression severity estimation performance. 2) We propose a novel FACS3D-Net to integrate $3D$ and $2D$ convolution network for facial Action Unit (AU) detection. As far as we know, this is the first work to apply $3D$ CNN to the problem of AU detection. Our future work will 1) focus on combining depression estimation with dimensional affective analysis through the proposed FACS3D-Net, and 2) collect Chinese depression database. When completed, these studies will compose the author's dissertation.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129460037","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: 7

Local Second-Order Gradient Cross Pattern for Automatic Depression Detection 局部二阶梯度交叉模式的自动降压检测

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925158

Mingyue Niu, J. Tao, Bin Liu

{"title":"Local Second-Order Gradient Cross Pattern for Automatic Depression Detection","authors":"Mingyue Niu, J. Tao, Bin Liu","doi":"10.1109/ACIIW.2019.8925158","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925158","url":null,"abstract":"Depression is a psychiatric disorder that seriously affects people's work and life. At present, the development of the automatic depression detection technology has become the focus of many researchers due to the serious imbalance of doctor-patient ratio. Physiological studies have revealed that there are differences in facial activity between normal and depressed individuals, so some works has been done to detect depression by extracting facial features. However, these works are limited in capturing the subtle changes. For these reasons, this paper proposes a novel local pattern named Local Second-Order Gradient Cross Pattern (LSOGCP) to extract the subtle facial dynamics in videos to improve the accuracy of depression detection. In particular, we firstly obtain LSOGCP feature through high-order gradient and cross coding scheme to characterize the detailed texture of each frame. Then LSOGCP histograms from three orthogonal planes (TOP) are generated to form the video representation denoted as LSOGCP-TOP. Finally, a hierarchical method of between-group classification and within-group regression is employed to predict the score of depression severity. Experiments are conducted on two publicly available databases i.e. AVEC2013 and AVEC2014. The results demonstrate that our proposed method achieves better performance than the previous algorithms.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126924533","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: 8

What Affects the Performance of Convolutional Neural Networks for Audio Event Classification 什么影响了卷积神经网络在音频事件分类中的性能

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925277

Helin Wang, Dading Chong, Dongyan Huang, Yuexian Zou

引用次数: 6

Gram Matrices Formulation of Body Shape Motion: An Application for Depression Severity Assessment 体型运动的克氏矩阵公式:在抑郁症严重程度评估中的应用

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925009

M. Daoudi, Z. Hammal, Anis Kacem, J. Cohn

引用次数: 4