Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344604
Jin Wang, K. R. Lai, Liang-Chih Yu, Xuejie Zhang
Text-based sentiment analysis is a growing research field in affective computing, driven by both commercial applications and academic interest. Continuous dimensional representations, such as valence-arousal (VA) space, can represent the affective state more precisely than discrete effective representations. In building dimensional sentiment applications, affective lexicons with valence-arousal ratings are useful resources but are still very rare. Therefore, recent studies have investigated the automatic development of VA lexicons using linear regression techniques. One of the major limitations of linear regression is the under-fitting problem which can cause a poor fit between the algorithm and the training data. To tackle this problem, this study proposes the use of a locally weighted linear regression (LWLR) model to predict the valence-arousal ratings of affective words. The locally weighted method performs a regression around the point of interest using only training data that are “local” to that point, and thus can reduce the impact of noise from unrelated training data. Experimental results show that the proposed method achieved better performance for VA word prediction.
{"title":"A locally weighted method to improve linear regression for lexical-based valence-arousal prediction","authors":"Jin Wang, K. R. Lai, Liang-Chih Yu, Xuejie Zhang","doi":"10.1109/ACII.2015.7344604","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344604","url":null,"abstract":"Text-based sentiment analysis is a growing research field in affective computing, driven by both commercial applications and academic interest. Continuous dimensional representations, such as valence-arousal (VA) space, can represent the affective state more precisely than discrete effective representations. In building dimensional sentiment applications, affective lexicons with valence-arousal ratings are useful resources but are still very rare. Therefore, recent studies have investigated the automatic development of VA lexicons using linear regression techniques. One of the major limitations of linear regression is the under-fitting problem which can cause a poor fit between the algorithm and the training data. To tackle this problem, this study proposes the use of a locally weighted linear regression (LWLR) model to predict the valence-arousal ratings of affective words. The locally weighted method performs a regression around the point of interest using only training data that are “local” to that point, and thus can reduce the impact of noise from unrelated training data. Experimental results show that the proposed method achieved better performance for VA word prediction.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"1 1","pages":"415-420"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72887881","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344657
Damien Dupré, A. Tcherkassof, Michel Dubois
User eXperience studies with products, systems or services have significantly increased in companies in order to anticipate their commercial success. Among the user experience dimensions, emotions are predominant. However User eXperience studies used several concepts to refer to emotions and current measures still have some flaws. Consequently, this doctoral project aims firstly to provide a multi-componential approach of emotions based on a psychological view, and secondly to provide Affective Computing solutions in order to evaluate emotions in User eXperience studies. Through a study using hand-gesture interface devices, three components of users' emotions were simultaneously measured with self-reports: the subjective, cognitive and motivational components. The results point out the possibility of measuring different components in order to gain a better understanding of emotions triggered by products. They also point out that self-reports measures could be improved with Affective Computing solutions. In this perspective, two emotion assessment tools were developed: Oudjat and EmoLyse.
{"title":"Emotions triggered by innovative products: A multi-componential approach of emotions for user experience tools","authors":"Damien Dupré, A. Tcherkassof, Michel Dubois","doi":"10.1109/ACII.2015.7344657","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344657","url":null,"abstract":"User eXperience studies with products, systems or services have significantly increased in companies in order to anticipate their commercial success. Among the user experience dimensions, emotions are predominant. However User eXperience studies used several concepts to refer to emotions and current measures still have some flaws. Consequently, this doctoral project aims firstly to provide a multi-componential approach of emotions based on a psychological view, and secondly to provide Affective Computing solutions in order to evaluate emotions in User eXperience studies. Through a study using hand-gesture interface devices, three components of users' emotions were simultaneously measured with self-reports: the subjective, cognitive and motivational components. The results point out the possibility of measuring different components in order to gain a better understanding of emotions triggered by products. They also point out that self-reports measures could be improved with Affective Computing solutions. In this perspective, two emotion assessment tools were developed: Oudjat and EmoLyse.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"25 1","pages":"772-777"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73065936","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344626
Dongrui Wu, Vernon J. Lawhern, Brent Lance
Rapid serial visual presentation based brain-computer interface (BCI) system relies on single-trial classification of event-related potentials. Because of large individual differences, some labeled subject-specific data are needed to calibrate the classifier for each new subject. This paper proposes an online weighted adaptation regularization (OwAR) algorithm to reduce the online calibration effort, and hence to increase the utility of the BCI system. We show that given the same number of labeled subject-specific training samples, OwAR can significantly improve the online calibration performance. In other words, given a desired classification accuracy, OwAR can significantly reduce the number of labeled subject-specific training samples. Furthermore, we also show that the computational cost of OwAR can be reduced by more than 50% by source domain selection, without a statistically significant sacrifice of classification performance.
{"title":"Reducing BCI calibration effort in RSVP tasks using online weighted adaptation regularization with source domain selection","authors":"Dongrui Wu, Vernon J. Lawhern, Brent Lance","doi":"10.1109/ACII.2015.7344626","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344626","url":null,"abstract":"Rapid serial visual presentation based brain-computer interface (BCI) system relies on single-trial classification of event-related potentials. Because of large individual differences, some labeled subject-specific data are needed to calibrate the classifier for each new subject. This paper proposes an online weighted adaptation regularization (OwAR) algorithm to reduce the online calibration effort, and hence to increase the utility of the BCI system. We show that given the same number of labeled subject-specific training samples, OwAR can significantly improve the online calibration performance. In other words, given a desired classification accuracy, OwAR can significantly reduce the number of labeled subject-specific training samples. Furthermore, we also show that the computational cost of OwAR can be reduced by more than 50% by source domain selection, without a statistically significant sacrifice of classification performance.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"1 1","pages":"567-573"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74436497","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344590
C. Zhu, Harshit Agrawal, P. Maes
Data-Objects introduces the idea of re-designing physical objects that a person uses every day to act as tactile affective interfaces. Data-Objects are a means to provide users information about their use of different everyday objects and how it affects them. We do this by re-designing the objects to embed information in the physical body of the object itself without hampering its original functional capability. By 3D printing the body in a set period of time, differences in the information represented on the physical body over the time are aimed at highlighting patterns of how the usage of that object has been affecting the user. The overwhelming digital information that we are exposed to and the disconnect that it has from what it is representing makes the relation of the information to the different aspects of our life not effective. We think that using physical forms of objects to provide information can make the data more meaningful, enhancing the value of the object beyond its intended function. Physical forms can provide subliminal and tactile feedback to the users as they use the objects throughout the day, without specific visual attention. Being present physically ensures that people are more conscious of the data and patterns, and makes the data visible to other people as well.
{"title":"Data-objects: Re-designing everyday objects as tactile affective interfaces","authors":"C. Zhu, Harshit Agrawal, P. Maes","doi":"10.1109/ACII.2015.7344590","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344590","url":null,"abstract":"Data-Objects introduces the idea of re-designing physical objects that a person uses every day to act as tactile affective interfaces. Data-Objects are a means to provide users information about their use of different everyday objects and how it affects them. We do this by re-designing the objects to embed information in the physical body of the object itself without hampering its original functional capability. By 3D printing the body in a set period of time, differences in the information represented on the physical body over the time are aimed at highlighting patterns of how the usage of that object has been affecting the user. The overwhelming digital information that we are exposed to and the disconnect that it has from what it is representing makes the relation of the information to the different aspects of our life not effective. We think that using physical forms of objects to provide information can make the data more meaningful, enhancing the value of the object beyond its intended function. Physical forms can provide subliminal and tactile feedback to the users as they use the objects throughout the day, without specific visual attention. Being present physically ensures that people are more conscious of the data and patterns, and makes the data visible to other people as well.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"43 1","pages":"322-326"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74820627","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344682
Dongrui Wu, Chun-Hsiang Chuang, Chin-Teng Lin
Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.
{"title":"Online driver's drowsiness estimation using domain adaptation with model fusion","authors":"Dongrui Wu, Chun-Hsiang Chuang, Chin-Teng Lin","doi":"10.1109/ACII.2015.7344682","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344682","url":null,"abstract":"Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"5 1","pages":"904-910"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73928499","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344569
Giota Stratou, Rens Hoegen, Gale M. Lucas, J. Gratch
Emotional signaling plays an important role in negotiations and other social decision-making tasks as it can signal intention and shape joint decisions. Specifically it has been shown to influence cooperation or competition. This has been shown in previous studies for scripted interactions that control emotion signaling and rely on manual coding of affect. In this work we examine face-to-face interactions in an iterative social dilemma task (prisoner's dilemma) via an automatic framework for facial expression analysis. We explore if automatic analysis of emotion can give insight into the social function of emotion in face-to-face interactions. Our analysis suggests that positive and negative displays of emotion are associated with more prosocial and proself game acts respectively. Moreover signaling cooperative intentions to the opponent via positivity can leave participants more open to exploitation, whereas signaling a more tough stance via negativity seems to discourage exploitation. However, the benefit of negative affect is short-term and both players do worse over time if they show negative emotions.
{"title":"Emotional signaling in a social dilemma: An automatic analysis","authors":"Giota Stratou, Rens Hoegen, Gale M. Lucas, J. Gratch","doi":"10.1109/ACII.2015.7344569","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344569","url":null,"abstract":"Emotional signaling plays an important role in negotiations and other social decision-making tasks as it can signal intention and shape joint decisions. Specifically it has been shown to influence cooperation or competition. This has been shown in previous studies for scripted interactions that control emotion signaling and rely on manual coding of affect. In this work we examine face-to-face interactions in an iterative social dilemma task (prisoner's dilemma) via an automatic framework for facial expression analysis. We explore if automatic analysis of emotion can give insight into the social function of emotion in face-to-face interactions. Our analysis suggests that positive and negative displays of emotion are associated with more prosocial and proself game acts respectively. Moreover signaling cooperative intentions to the opponent via positivity can leave participants more open to exploitation, whereas signaling a more tough stance via negativity seems to discourage exploitation. However, the benefit of negative affect is short-term and both players do worse over time if they show negative emotions.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"11 1","pages":"180-186"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76748041","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344573
Ercheng Pei, Le Yang, D. Jiang, H. Sahli
In this paper we propose the deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) based single modal and multi-modal affect recognition frameworks. In the single modal framework DBLSTM with moving average (MA), audio or visual features are input into the DBLSTM-RNN model, whose output estimations of a dimension are smoothed by the moving average filter. After the smoothed estimations are expanded to the frame rate of the ground truth labels, another MA is adopted for smoothing the final results. In the multi-modal framework DBLSTM-DBLSTM-MA, the initial estimations from the audio and visual modalities via the first layer of DBLSTM-RNNs are input into a second layer of DBLSTM-RNN, whose outputs are smoothed by MA. The smoothed estimations are then expanded to the frame rate of the ground truth labels and smoothed again by another MA. Affect recognition experiments are carried out on the training set and development set of the AVEC2014 database, results show that the proposed DBLSTM-MA framework outperforms linear regression, support vector regression (SVR), and BLSTM for single modal dimension estimation. For audio visual multi-modal affect recognition, DBLSTM-DBLSTM-MA obtains better or comparable performance than the state of the art results in the competition of AVEC2014, with the average correlation coefficient (COR) reaches 0.599 on the Freeform database, 0.630 on the Northwind database, and 0.615 on the Freeform-Northwind database.
{"title":"Multimodal dimensional affect recognition using deep bidirectional long short-term memory recurrent neural networks","authors":"Ercheng Pei, Le Yang, D. Jiang, H. Sahli","doi":"10.1109/ACII.2015.7344573","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344573","url":null,"abstract":"In this paper we propose the deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) based single modal and multi-modal affect recognition frameworks. In the single modal framework DBLSTM with moving average (MA), audio or visual features are input into the DBLSTM-RNN model, whose output estimations of a dimension are smoothed by the moving average filter. After the smoothed estimations are expanded to the frame rate of the ground truth labels, another MA is adopted for smoothing the final results. In the multi-modal framework DBLSTM-DBLSTM-MA, the initial estimations from the audio and visual modalities via the first layer of DBLSTM-RNNs are input into a second layer of DBLSTM-RNN, whose outputs are smoothed by MA. The smoothed estimations are then expanded to the frame rate of the ground truth labels and smoothed again by another MA. Affect recognition experiments are carried out on the training set and development set of the AVEC2014 database, results show that the proposed DBLSTM-MA framework outperforms linear regression, support vector regression (SVR), and BLSTM for single modal dimension estimation. For audio visual multi-modal affect recognition, DBLSTM-DBLSTM-MA obtains better or comparable performance than the state of the art results in the competition of AVEC2014, with the average correlation coefficient (COR) reaches 0.599 on the Freeform database, 0.630 on the Northwind database, and 0.615 on the Freeform-Northwind database.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"319 1","pages":"208-214"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77267832","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344652
Zhenyu Liu, B. Hu, Lihua Yan, Tian-Zhong Wang, Fei Liu, Xiaoyu Li, Huanyu Kang
Depression is a common mental disorder and one of the main causes of disability worldwide. Lacking objective depressive disorder assessment methods is the key reason that many depressive patients can't be treated properly. Developments in affective sensing technology with a focus on acoustic features will potentially bring a change due to depressed patient's slow, hesitating, monotonous voice as remarkable characteristics. Our motivation is to find out a speech feature set to detect, evaluate and even predict depression. For these goals, we investigate a large sample of 300 subjects (100 depressed patients, 100 healthy controls and 100 high-risk people) through comparative analysis and follow-up study. For examining the correlation between depression and speech, we extract features as many as possible according to previous research to create a large voice feature set. Then we employ some feature selection methods to eliminate irrelevant, redundant and noisy features to form a compact subset. To measure effectiveness of this new subset, we test it on our dataset with 300 subjects using several common classifiers and 10-fold cross-validation. Since we are collecting data currently, we have no result to report yet.
{"title":"Detection of depression in speech","authors":"Zhenyu Liu, B. Hu, Lihua Yan, Tian-Zhong Wang, Fei Liu, Xiaoyu Li, Huanyu Kang","doi":"10.1109/ACII.2015.7344652","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344652","url":null,"abstract":"Depression is a common mental disorder and one of the main causes of disability worldwide. Lacking objective depressive disorder assessment methods is the key reason that many depressive patients can't be treated properly. Developments in affective sensing technology with a focus on acoustic features will potentially bring a change due to depressed patient's slow, hesitating, monotonous voice as remarkable characteristics. Our motivation is to find out a speech feature set to detect, evaluate and even predict depression. For these goals, we investigate a large sample of 300 subjects (100 depressed patients, 100 healthy controls and 100 high-risk people) through comparative analysis and follow-up study. For examining the correlation between depression and speech, we extract features as many as possible according to previous research to create a large voice feature set. Then we employ some feature selection methods to eliminate irrelevant, redundant and noisy features to form a compact subset. To measure effectiveness of this new subset, we test it on our dataset with 300 subjects using several common classifiers and 10-fold cross-validation. Since we are collecting data currently, we have no result to report yet.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"32 1","pages":"743-747"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80769267","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344549
A. Brouwer, C. Dijksterhuis, J. V. Erp
Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous research [1], Brouwer and colleagues examined effects of mental effort as manipulated in a cognitive (memory) task on a range of physiological signals. In the present research we examine the same types of physiological signals using the same kind of analysis in a visuomotor (simulated driving) task. In this case, mental effort was manipulated using wide and narrow lanes. Effects of task difficulty on both subjective mental effort and behavioral variables were comparable across tasks. Effect of task difficulty was replicated for respiration frequency and to some extent for EEG alpha activity. However, in contrast to the cognitive task [1], skin conductance and heart rate related variables were not significantly affected by task difficulty in the current visuomotor task. We argue that differences in visual attention and cerebral energy demand between the types of tasks may be at the basis of this.
{"title":"Physiological correlates of mental effort as manipulated through lane width during simulated driving","authors":"A. Brouwer, C. Dijksterhuis, J. V. Erp","doi":"10.1109/ACII.2015.7344549","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344549","url":null,"abstract":"Previous studies suggest that physiological effects of mental effort as manipulated trough cognitive task difficulty differ from effects of mental effort as manipulated trough a visuomotor task such as lane keeping in simulated driving. Most notably, heart rate increases with mental effort in the former but not in the latter task. EEG seems to be indicative of mental effort in both cases. In previous research [1], Brouwer and colleagues examined effects of mental effort as manipulated in a cognitive (memory) task on a range of physiological signals. In the present research we examine the same types of physiological signals using the same kind of analysis in a visuomotor (simulated driving) task. In this case, mental effort was manipulated using wide and narrow lanes. Effects of task difficulty on both subjective mental effort and behavioral variables were comparable across tasks. Effect of task difficulty was replicated for respiration frequency and to some extent for EEG alpha activity. However, in contrast to the cognitive task [1], skin conductance and heart rate related variables were not significantly affected by task difficulty in the current visuomotor task. We argue that differences in visual attention and cerebral energy demand between the types of tasks may be at the basis of this.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":" 15","pages":"42-48"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91415148","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}
Pub Date : 2015-09-21DOI: 10.1109/ACII.2015.7344623
A. Sano, Paul Johns, M. Czerwinski
We developed a feedback-loop, user-tailored advice system to provide stress interventions and advice about improving sleep, diet, and exercise habits at the workplace. Thirty participants joined a 2 week study: in the first week, we collected their behaviors about sleep, diet, exercise and stress levels using Fitbit and surveys. During the second week we continued monitoring, and based on the participants' measurements in the previous days, we also provided interventions and advice during the workday, and evaluated their preferences. We found that participants with higher stress levels liked stress interventions more and that somatic activities were most preferred and reduced stress levels the most. We observed individual preference differences in the types of advice; however, tracking and receiving advice raised users' awareness of their stress, sleep, exercise, and dietary behaviors. We found that the largest positive impact was on our participants' dietary behaviors.
{"title":"HealthAware: An advice system for stress, sleep, diet and exercise","authors":"A. Sano, Paul Johns, M. Czerwinski","doi":"10.1109/ACII.2015.7344623","DOIUrl":"https://doi.org/10.1109/ACII.2015.7344623","url":null,"abstract":"We developed a feedback-loop, user-tailored advice system to provide stress interventions and advice about improving sleep, diet, and exercise habits at the workplace. Thirty participants joined a 2 week study: in the first week, we collected their behaviors about sleep, diet, exercise and stress levels using Fitbit and surveys. During the second week we continued monitoring, and based on the participants' measurements in the previous days, we also provided interventions and advice during the workday, and evaluated their preferences. We found that participants with higher stress levels liked stress interventions more and that somatic activities were most preferred and reduced stress levels the most. We observed individual preference differences in the types of advice; however, tracking and receiving advice raised users' awareness of their stress, sleep, exercise, and dietary behaviors. We found that the largest positive impact was on our participants' dietary behaviors.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"86 1","pages":"546-552"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86863134","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：