The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

P C Chung - One of the best experts on this subject based on the ideXlab platform.

  • Application of support vector regression for phyciological emotion recognition
    2010 International Computer Symposium (ICS2010), 2010
    Co-Authors: Chuan-yu Chang, Jun-Ying Zheng, C J Wang, P C Chung
    Abstract:

    Cases of physical and Mental Diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). ExperiMental results show that the proposed method has a high recognition rate of 90.6%.

Chuan-yu Chang - One of the best experts on this subject based on the ideXlab platform.

  • Physiological Angry Emotion Detection Using Support Vector Regression
    2012 15th International Conference on Network-Based Information Systems, 2012
    Co-Authors: Chuan-yu Chang, Yu-Mon Lin, Jun-Ying Zheng
    Abstract:

    Physical and Mental Diseases caused by stress and negative emotions have increased in recent years. Emotion can be roughly recognized by facial expressions. However, facial expressions may be controlled and expressed differently by different people subjectively, inaccurate results are unavoidable. On the contrary, physiological responses and the corresponding signals are hardly to control while emotions are excited. Therefore, a physiological angry emotion detection method is proposed in this paper. A specific designed emotion induction experiment is performed to collect four physiological signals of subjects including electrocardiogram, galvanic skin responses (GSR), blood volume pulse, and pulse. The Support Vector Regression (SVR) is used to train the trend curves of angry emotion. ExperiMental results show that the proposed method achieves high recognition rate.

  • Application of support vector regression for phyciological emotion recognition
    2010 International Computer Symposium (ICS2010), 2010
    Co-Authors: Chuan-yu Chang, Jun-Ying Zheng, C J Wang, P C Chung
    Abstract:

    Cases of physical and Mental Diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). ExperiMental results show that the proposed method has a high recognition rate of 90.6%.

Jun-Ying Zheng - One of the best experts on this subject based on the ideXlab platform.

  • Physiological Angry Emotion Detection Using Support Vector Regression
    2012 15th International Conference on Network-Based Information Systems, 2012
    Co-Authors: Chuan-yu Chang, Yu-Mon Lin, Jun-Ying Zheng
    Abstract:

    Physical and Mental Diseases caused by stress and negative emotions have increased in recent years. Emotion can be roughly recognized by facial expressions. However, facial expressions may be controlled and expressed differently by different people subjectively, inaccurate results are unavoidable. On the contrary, physiological responses and the corresponding signals are hardly to control while emotions are excited. Therefore, a physiological angry emotion detection method is proposed in this paper. A specific designed emotion induction experiment is performed to collect four physiological signals of subjects including electrocardiogram, galvanic skin responses (GSR), blood volume pulse, and pulse. The Support Vector Regression (SVR) is used to train the trend curves of angry emotion. ExperiMental results show that the proposed method achieves high recognition rate.

  • Application of support vector regression for phyciological emotion recognition
    2010 International Computer Symposium (ICS2010), 2010
    Co-Authors: Chuan-yu Chang, Jun-Ying Zheng, C J Wang, P C Chung
    Abstract:

    Cases of physical and Mental Diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). ExperiMental results show that the proposed method has a high recognition rate of 90.6%.

C J Wang - One of the best experts on this subject based on the ideXlab platform.

  • Application of support vector regression for phyciological emotion recognition
    2010 International Computer Symposium (ICS2010), 2010
    Co-Authors: Chuan-yu Chang, Jun-Ying Zheng, C J Wang, P C Chung
    Abstract:

    Cases of physical and Mental Diseases caused by stress and negative emotions have increased annually. Many emotion recognition methods have been proposed. Facial expression is widely used for emotion recognition. However, since facial expressions may be expressed differently by different people, inaccurate results are unavoidable. Nerve and Physiological responses are incontrollable native response. Physiological responses and the corresponding signals are difficult to control when a person is overcome with emotion. Therefore, an emotion recognition system that considers physiological signals is proposed in this paper. An emotion induction experiment was performed to collect five physiological signals from subjects, namely electrocardiogram, respiration, galvanic skin response (GSR), blood volume pulse, and pulse. Support vector regression (SVR) was used to train three trend curves of three emotions (sadness, fear, and pleasure). ExperiMental results show that the proposed method has a high recognition rate of 90.6%.

Judit Simon - One of the best experts on this subject based on the ideXlab platform.

  • Prevalence of Mental Diseases in Austria. Systematic Review of the Published Evidence
    Wiener Klinische Wochenschrift, 2018
    Co-Authors: Agata Łaszewska, August Österle, Johannes Wancata, Judit Simon
    Abstract:

    Background: Addressing the growing burden of Mental Diseases is a public health priority. Nevertheless, many countries lack reliable estimates of the proportion of the population affected, which are crucial for health and social policy planning. This study aimed to collect existing evidence on the prevalence of Mental Diseases in Austria. Methods: A systematic review was conducted using MeSH, EMTREE and free-text terms in seven bibliographic databases. In addition, the references of included papers and relevant Austria-specific websites were searched. Articles published after 1996 pertaining to the Austrian adult population and presenting prevalence data for Mental Diseases were included in the analysis. Results: A total of 2612 records were identified in the database search, 19 of which were included in the analysis, 13 were community-based studies and 6 examined institutionalized populations. Sample sizes ranged from 200 to 15,474. The evidence was centered around depression (n= 6, 32%), eating disorders (n= 4, 21%) and alcohol dependence (n= 3, 16%). While most studies (n= 10, 53%) used questionnaires and scales to identify Mental Diseases, seven studies used structured clinical interviews, and two studies examined use of psychotropic drugs. Due to the diversity of methodologies, no statistical pooling of prevalence estimates was possible. Conclusion: Information on the prevalence of Mental Diseases in Austria is limited and comparability between studies is restricted. A variety of diagnostic instruments, targeted populations and investigated Diseases contribute to discrepancies in the prevalence rates. A systematic, large-scale study on the prevalence of Mental Diseases in Austria is needed for comprehensive and robust epidemiological evidence.

  • Prevalence of Mental Diseases in Austria : Systematic review of the published evidence.
    Wiener klinische Wochenschrift, 2018
    Co-Authors: Agata Łaszewska, August Österle, Johannes Wancata, Judit Simon
    Abstract:

    Addressing the growing burden of Mental Diseases is a public health priority. Nevertheless, many countries lack reliable estimates of the proportion of the population affected, which are crucial for health and social policy planning. This study aimed to collect existing evidence on the prevalence of Mental Diseases in Austria. A systematic review was conducted using MeSH, EMTREE and free-text terms in seven bibliographic databases. In addition, the references of included papers and relevant Austria-specific websites were searched. Articles published after 1996 pertaining to the Austrian adult population and presenting prevalence data for Mental Diseases were included in the analysis. A total of 2612 records were identified in the database search, 19 of which were included in the analysis, 13 were community-based studies and 6 examined institutionalized populations. Sample sizes ranged from 200 to 15,474. The evidence was centered around depression (n = 6, 32%), eating disorders (n = 4, 21%) and alcohol dependence (n = 3, 16%). While most studies (n = 10, 53%) used questionnaires and scales to identify Mental Diseases, seven studies used structured clinical interviews, and two studies examined use of psychotropic drugs. Due to the diversity of methodologies, no statistical pooling of prevalence estimates was possible. Information on the prevalence of Mental Diseases in Austria is limited and comparability between studies is restricted. A variety of diagnostic instruments, targeted populations and investigated Diseases contribute to discrepancies in the prevalence rates. A systematic, large-scale study on the prevalence of Mental Diseases in Austria is needed for comprehensive and robust epidemiological evidence.