Virtual Reality (VR) provides users with a sensory experience that is close to reality, creating a sense of interaction. It is widely used, and the gesture recognition in VR also has a great effect. Gesture recognition enriches VR using experience and promotes a more direct and natural interaction. Gesture recognition usually employs sensors to collect data from users and machine leaning algorithms to interpret and respond to human activities. Complex gestures need more complex algorithms and more rigorous operations. The reason is that complex gestures mean larger quantity of data. If data is larger, the harder to get robust and effective datasets. Then, features can also become difficult to extract, contributing to misrecognition or unrecognizable. Though machine leaning algorithms are widely used in gesture recognition, there are still some important challenges need to be addressed, like lack of standardization and limitations of availability of diverse and large datasets. However, VR, gesture recognition and machine leaning algorithms all have excellent prospect, because they are in line with the development of the Times and show the progress of science and technology. This paper not only focuses on their advantages but also does not ignore their shortcomings, and looks at them comprehensively.
{"title":"Gesture Recognition in Virtual reality","authors":"Meng Wu","doi":"10.59388/pm00336","DOIUrl":"https://doi.org/10.59388/pm00336","url":null,"abstract":"Virtual Reality (VR) provides users with a sensory experience that is close to reality, creating a sense of interaction. It is widely used, and the gesture recognition in VR also has a great effect. Gesture recognition enriches VR using experience and promotes a more direct and natural interaction. Gesture recognition usually employs sensors to collect data from users and machine leaning algorithms to interpret and respond to human activities. Complex gestures need more complex algorithms and more rigorous operations. The reason is that complex gestures mean larger quantity of data. If data is larger, the harder to get robust and effective datasets. Then, features can also become difficult to extract, contributing to misrecognition or unrecognizable. Though machine leaning algorithms are widely used in gesture recognition, there are still some important challenges need to be addressed, like lack of standardization and limitations of availability of diverse and large datasets. However, VR, gesture recognition and machine leaning algorithms all have excellent prospect, because they are in line with the development of the Times and show the progress of science and technology. This paper not only focuses on their advantages but also does not ignore their shortcomings, and looks at them comprehensively.","PeriodicalId":508616,"journal":{"name":"Psychomachina","volume":"46 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139180679","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}
The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.
{"title":"Emotion Recognition in Psychology of Human-robot Interaction","authors":"Mengyao Zhao","doi":"10.59388/pm00331","DOIUrl":"https://doi.org/10.59388/pm00331","url":null,"abstract":"The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.","PeriodicalId":508616,"journal":{"name":"Psychomachina","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252650","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}
Teenagers, characterized by burgeoning curiosity about sexual material on the internet and often a lack of self-control, are increasingly free to explore the web. This study endeavors to discern the relationship between religiosity and self-control in relation to cybersex behavior among students. The respondents comprised 80 students from two Public High Schools located in Surabaya and Pasuruan, Indonesia, aged between 15-18 years. The study employs a quantitative correlation method, with research data collated through Google Forms, utilizing scales of religiosity, self-control, and cybersex behavior that have satisfied the criteria of validity and reliability. The data were subsequently analyzed using Spearman's rank non-parametric statistical analysis. The results of this study show a substantial negative relationship between religiosity and cybersex behavior, with a correlation coefficient value between the two of -0.509 and a significance level of .000 (p = .05). Similar results showed that self-control and cybersex conduct had a correlation value of -.402, with a significance level of .000 (p = .05) signifying a substantial negative relationship. Therefore, there is a strong inverse association between self-control and cybersex.
{"title":"Relationship Between Religiosity and Self-Control with Cybersex Behavior in School Students","authors":"Moh Alimudin Fauzi, S. Suroso, Muhammad Farid","doi":"10.59388/pm00247","DOIUrl":"https://doi.org/10.59388/pm00247","url":null,"abstract":"Teenagers, characterized by burgeoning curiosity about sexual material on the internet and often a lack of self-control, are increasingly free to explore the web. This study endeavors to discern the relationship between religiosity and self-control in relation to cybersex behavior among students. The respondents comprised 80 students from two Public High Schools located in Surabaya and Pasuruan, Indonesia, aged between 15-18 years. The study employs a quantitative correlation method, with research data collated through Google Forms, utilizing scales of religiosity, self-control, and cybersex behavior that have satisfied the criteria of validity and reliability. The data were subsequently analyzed using Spearman's rank non-parametric statistical analysis. The results of this study show a substantial negative relationship between religiosity and cybersex behavior, with a correlation coefficient value between the two of -0.509 and a significance level of .000 (p = .05). Similar results showed that self-control and cybersex conduct had a correlation value of -.402, with a significance level of .000 (p = .05) signifying a substantial negative relationship. Therefore, there is a strong inverse association between self-control and cybersex.","PeriodicalId":508616,"journal":{"name":"Psychomachina","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139336878","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}
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