2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)最新文献_第2页

Automatic Detection of Protective Movement Behavior with MoCap and sEMG Data for Chronic Pain Rehabilitation 基于动作捕捉和肌电图的慢性疼痛康复中保护性运动行为的自动检测

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925091

Chongyang Wang

{"title":"Automatic Detection of Protective Movement Behavior with MoCap and sEMG Data for Chronic Pain Rehabilitation","authors":"Chongyang Wang","doi":"10.1109/ACIIW.2019.8925091","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925091","url":null,"abstract":"Physical rehabilitation is an important part of chronic pain (CP) management. Physiotherapists provide guidance and intervention on the exercises based on the CP patient's affective states. One important clue the physiotherapist uses to understand their patient's affective state is the presence and type of protective movement behavior. As rehabilitation is transferring from clinical settings to home-based environments, technology should provide similar service by automatically detecting the protective behavior exhibited by patients and use it as a cue to inform and adapt the support. Our research focuses on the detection of protective behavior from a deep learning (DL) perspective and using MoCap and EMG data. Based on the knowledge learned from a wider-relevant literature and the specific characteristic of protective behavior, we aim to automatically detect protective behavior with deep learning approaches and further learn its configuration pattern with explainable models. Our initial studies have demonstrated interesting accuracy improvements and also provided important knowledges about the temporal and configurational characteristics of protective behavior.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"32 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":"133854710","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

Using a Quartile-based Data Transformation for Pain Intensity Classification based on the SenseEmotion Database 基于SenseEmotion数据库的基于四分位数的疼痛强度分类数据转换

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925244

Peter Bellmann, Patrick Thiam, F. Schwenker

引用次数: 6

Deep Neural Networks for Depression Recognition Based on Facial Expressions Caused by Stimulus Tasks 基于刺激任务引起的面部表情的深度神经网络抑郁症识别

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925293

Weitong Guo, Hongwu Yang, Zhenyu Liu

{"title":"Deep Neural Networks for Depression Recognition Based on Facial Expressions Caused by Stimulus Tasks","authors":"Weitong Guo, Hongwu Yang, Zhenyu Liu","doi":"10.1109/ACIIW.2019.8925293","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925293","url":null,"abstract":"With the growth of the global population, the proportion of individuals with depression has rapidly increased; it is currently the most prevalent mental health disorder. Although existing studies on depression have mainly examined the several databases, which comprise facial images and videos of non-Chinese subjects, there are few effective databases for a Chinese population. In this study, we first create a depression database by asking participants to perform five mood-elicitation tasks. After each task, their facial expressions are collected via a Kinect. In the depression database, the facial feature points (FFP) and facial action units (AU) are obtained. We build a range of deep belief network (DBN) models based on FFPs and AUs to extract facial features from facial expressions, named 5DBN, AU-5DBN and 5DBN-AU. We evaluate all proposed models in our built database, and the results demonstrate that (1) the recognition performance of the AU-5DBN model is higher than that of the 5DBN-AU model, and that of the single feature model is the lowest; (2) The performance of depression recognition in the positive and negative emotional stimuluses are higher than that of neutral emotional stimulus; (3) The classification rate for females is generally higher than that for males. Most importantly, the constructed database is from a real environment, i.e., several psychiatric hospitals, and has a certain scale. The experimental results show higher recognition performance in the database; thus, the proposed method is validated as effective in identifying depression.","PeriodicalId":193568,"journal":{"name":"2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)","volume":"29 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":"122020211","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: 5

The Role of Trust and Social Behaviours in Children's Learning from Social Robots 信任和社会行为在儿童社交机器人学习中的作用

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925269

Rebecca Stower

引用次数: 7

Introducing the EmteqVR Interface for Affect Detection in Virtual Reality 介绍了EmteqVR虚拟现实情感检测接口

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925297

I. Mavridou, E. Seiss, Theodoros Kostoulas, M. Hamedi, E. Balaguer-Ballester, C. Nduka

引用次数: 2

Combining Gated Convolutional Networks and Self-Attention Mechanism for Speech Emotion Recognition 结合门控卷积网络和自注意机制的语音情绪识别

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925283

C. Li, Jinlong Jiao, Yiqin Zhao, Ziping Zhao

引用次数: 2

Smile Intensity Detection in Multiparty Interaction using Deep Learning 基于深度学习的多方交互微笑强度检测

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925261

Philine Witzig, James Kennedy, Cristina Segalin

引用次数: 2

Robust Emotion Navigation: Few-shot Visual Sentiment Analysis by Auxiliary Noisy Data 鲁棒情绪导航:基于辅助噪声数据的少镜头视觉情绪分析

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925021

Lin Wang, Xiangmin Xu, Fang Liu, Xiaofen Xing, Bolun Cai, Weirui Lu

{"title":"Robust Emotion Navigation: Few-shot Visual Sentiment Analysis by Auxiliary Noisy Data","authors":"Lin Wang, Xiangmin Xu, Fang Liu, Xiaofen Xing, Bolun Cai, Weirui Lu","doi":"10.1109/ACIIW.2019.8925021","DOIUrl":"https://doi.org/10.1109/ACIIW.2019.8925021","url":null,"abstract":"Few-shot visual sentiment analysis on social media is an important affective computing task. However, features acquired from few-shot samples are difficult, becasue the visual sentiment is a high-level integration task based on content and style. To address this issue, inspired by human learning processing, only a small number of multi-category emotions are learned from courses or specific occasions. In this paper, we propose a robust emotion navigation framework using auxiliary noisy data to re-focus on few-shot precise emotion knowledge. Firstly, we pre-trained the network on a large noisy data with cross-entropy loss, and the noise matrix can be estimated by predicted probability. Secondly, few-shot precise samples are applied as the prototype center to guide noisy data clustering. Here, the noise matrix is embedded into the loss function for re-weighting, which improves the noise robustness of the network. Finally, we relabel the noisy dataset with above joint training predictions and then re-train the network coarse-to-fine. We conduct experiments on three public sentiment datasets, including Sentibank, Twitter and Emotion6. The results demonstrate the effectiveness of the proposed method.","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":"116052291","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: 6

Detecting negativity in user comments using emotional maps and convolutional neural networks 使用情感地图和卷积神经网络检测用户评论中的消极性

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925167

Elena Manishina, Dylan Tweed, Guillaume Tiberi, Lorena Gayarre Pena, Nicolas Martin

引用次数: 0

PAGAN: Platform for Audiovisual General-purpose ANnotation 用于视听通用注释的平台

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) Pub Date : 2019-09-01 DOI: 10.1109/ACIIW.2019.8925149

Dávid Melhárt, Antonios Liapis, Georgios N. Yannakakis

引用次数: 1