PeerJ Computer Science:Human-Computer Interactionhttps://peerj.com/articles/index.atom?journal=cs&subject=7550Human-Computer Interaction articles published in PeerJ Computer ScienceThe experience of a tele-operated avatar being touched increases operator’s sense of discomforthttps://peerj.com/articles/cs-19262024-03-192024-03-19Mitsuhiko KimotoMasahiro Shiomi
Recent advancements in tele-operated avatars, both on-screen and robotic, have expanded opportunities for human interaction that exceed spatial and physical limitations. While numerous studies have enhanced operator control and improved the impression left on remote users, one area remains underexplored: the experience of operators during touch interactions between an avatar and a remote interlocutor. Touch interactions have become commonplace with avatars, especially those displayed on or integrated with touchscreen interfaces. Although the need for avatars to exhibit human-like touch responses has been recognized as beneficial for maintaining positive impressions on remote users, the sensations and experiences of the operators behind these avatars during such interactions remain largely uninvestigated. This study examines the sensations felt by an operator when their tele-operated avatar is touched remotely. Our findings reveal that operators can perceive a sensation of discomfort when their on-screen avatar is touched. This feeling is intensified when the touch is visualized and the avatar reacts to it. Although these autonomous responses may enhance the human-like perceptions of remote users, they might also lead to operator discomfort. This situation underscores the importance of designing avatars that address the experiences of both remote users and operators. We address this issue by proposing a tele-operated avatar system that minimizes unwarranted touch interactions from unfamiliar interlocutors based on social intimacy.
Recent advancements in tele-operated avatars, both on-screen and robotic, have expanded opportunities for human interaction that exceed spatial and physical limitations. While numerous studies have enhanced operator control and improved the impression left on remote users, one area remains underexplored: the experience of operators during touch interactions between an avatar and a remote interlocutor. Touch interactions have become commonplace with avatars, especially those displayed on or integrated with touchscreen interfaces. Although the need for avatars to exhibit human-like touch responses has been recognized as beneficial for maintaining positive impressions on remote users, the sensations and experiences of the operators behind these avatars during such interactions remain largely uninvestigated. This study examines the sensations felt by an operator when their tele-operated avatar is touched remotely. Our findings reveal that operators can perceive a sensation of discomfort when their on-screen avatar is touched. This feeling is intensified when the touch is visualized and the avatar reacts to it. Although these autonomous responses may enhance the human-like perceptions of remote users, they might also lead to operator discomfort. This situation underscores the importance of designing avatars that address the experiences of both remote users and operators. We address this issue by proposing a tele-operated avatar system that minimizes unwarranted touch interactions from unfamiliar interlocutors based on social intimacy.CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?https://peerj.com/articles/cs-18662024-02-292024-02-29Antonio FerrándezRocío Lavigne-CervánJesús PeralIgnasi Navarro-SoriaÁngel LloretDavid GilCarmen Rocamora
In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.
In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.Do popular apps have issues regarding energy efficiency?https://peerj.com/articles/cs-18912024-02-292024-02-29Cagri Sahin
Mobile apps have become essential components of our daily lives, seamlessly integrating into routines to fulfill communication, productivity, entertainment, and commerce needs, with their diverse range categorized within app stores for easy user navigation and selection. User reviews and ratings play a crucial role in app selection, significantly influencing user decisions through the interplay between feedback and quantified satisfaction. The emphasis on energy efficiency in apps, driven by the limited battery lifespan of mobile devices, impacts app ratings by potentially prompting users to assign low scores, thereby influencing the choices of others. In this study, the presence of energy consumption issues within widely-used popular apps that have high app ratings and user interaction has been investigated through the analysis of user reviews. It is anticipated that popular apps, with high ratings, are less problematic than other apps. User reviews were collected from 32 apps across 16 diverse categories and subsequently filtered based on specific keywords. From the resulting pool of 14,064 user reviews, 8,007 reviews were manually identified as specifically addressing the app’s energy consumption. The results of the study demonstrate that all 32 popular apps under consideration exhibit issues related to energy consumption. While the frequency of energy-related issues may vary, it is evident that users are concerned about app energy consumption, as evidenced by the reception of complaint reviews regarding their energy usage. App energy efficiency is important to users, including popular apps with diverse features, necessitating developers to address expectations and optimize for energy efficiency. Improving the energy efficiency of apps has the potential to enhance user satisfaction and, consequently, contribute to the overall success of the app.
Mobile apps have become essential components of our daily lives, seamlessly integrating into routines to fulfill communication, productivity, entertainment, and commerce needs, with their diverse range categorized within app stores for easy user navigation and selection. User reviews and ratings play a crucial role in app selection, significantly influencing user decisions through the interplay between feedback and quantified satisfaction. The emphasis on energy efficiency in apps, driven by the limited battery lifespan of mobile devices, impacts app ratings by potentially prompting users to assign low scores, thereby influencing the choices of others. In this study, the presence of energy consumption issues within widely-used popular apps that have high app ratings and user interaction has been investigated through the analysis of user reviews. It is anticipated that popular apps, with high ratings, are less problematic than other apps. User reviews were collected from 32 apps across 16 diverse categories and subsequently filtered based on specific keywords. From the resulting pool of 14,064 user reviews, 8,007 reviews were manually identified as specifically addressing the app’s energy consumption. The results of the study demonstrate that all 32 popular apps under consideration exhibit issues related to energy consumption. While the frequency of energy-related issues may vary, it is evident that users are concerned about app energy consumption, as evidenced by the reception of complaint reviews regarding their energy usage. App energy efficiency is important to users, including popular apps with diverse features, necessitating developers to address expectations and optimize for energy efficiency. Improving the energy efficiency of apps has the potential to enhance user satisfaction and, consequently, contribute to the overall success of the app.Validation of system usability scale as a usability metric to evaluate voice user interfaceshttps://peerj.com/articles/cs-19182024-02-292024-02-29Akshay Madhav DeshmukhRicardo Chalmeta
In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface’s usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.
In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface’s usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.A low-cost wireless extension for object detection and data logging for educational robotics using the ESP-NOW protocolhttps://peerj.com/articles/cs-18262024-02-162024-02-16Emma I. Capaldi
In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.
In recent years, inexpensive and easy to use robotics platforms have been incorporated into middle school, high school, and college educational curricula and competitions all over the world. Students have access to advanced microprocessors and sensor systems that engage, educate, and encourage their creativity. In this study, the capabilities of the widely available VEX Robotics System are extended using the wireless ESP-NOW protocol to allow for real-time data logging and to extend the computational capabilities of the system. Specifically, this study presents an open source system that interfaces a VEX V5 microprocessor, an OpenMV camera, and a computer. Images from OpenMV are sent to a computer where object detection algorithms can be run and instructions sent to the VEX V5 microprocessor while system data and sensor readings are sent from the VEX V5 microprocessor to the computer. System performance was evaluated as a function of distance between transmitter and receiver, data packet round trip timing, and object detection using YoloV8. Three sample applications are detailed including the evaluation of a vision-based object sorting machine, a drivetrain trajectory analysis, and a proportional-integral-derivative (PID) control algorithm tuning experiment. It was concluded that the system is well suited for real time object detection tasks and could play an important role in improving robotics education.SENSES-ASD: a social-emotional nurturing and skill enhancement system for autism spectrum disorderhttps://peerj.com/articles/cs-17922024-02-082024-02-08Haya Abu-NowarAdeeb SaitTawfik Al-HadhramiMohammed Al-SaremSultan Noman Qasem
This article introduces the Social-Emotional Nurturing and Skill Enhancement System (SENSES-ASD) as an innovative method for assisting individuals with autism spectrum disorder (ASD). Leveraging deep learning technologies, specifically convolutional neural networks (CNN), our approach promotes facial emotion recognition, enhancing social interactions and communication. The methodology involves the use of the Xception CNN model trained on the FER-2013 dataset. The designed system accepts a variety of media inputs, successfully classifying and predicting seven primary emotional states. Results show that our system achieved a peak accuracy rate of 71% on the training dataset and 66% on the validation dataset. The novelty of our work lies in the intricate combination of deep learning methods specifically tailored for high-functioning autistic adults and the development of a user interface that caters to their unique cognitive and sensory sensitivities. This offers a novel perspective on utilising technological advances for ASD intervention, especially in the domain of emotion recognition.
This article introduces the Social-Emotional Nurturing and Skill Enhancement System (SENSES-ASD) as an innovative method for assisting individuals with autism spectrum disorder (ASD). Leveraging deep learning technologies, specifically convolutional neural networks (CNN), our approach promotes facial emotion recognition, enhancing social interactions and communication. The methodology involves the use of the Xception CNN model trained on the FER-2013 dataset. The designed system accepts a variety of media inputs, successfully classifying and predicting seven primary emotional states. Results show that our system achieved a peak accuracy rate of 71% on the training dataset and 66% on the validation dataset. The novelty of our work lies in the intricate combination of deep learning methods specifically tailored for high-functioning autistic adults and the development of a user interface that caters to their unique cognitive and sensory sensitivities. This offers a novel perspective on utilising technological advances for ASD intervention, especially in the domain of emotion recognition.Laser communications system with drones as relay medium for healthcare applicationshttps://peerj.com/articles/cs-17592024-02-072024-02-07Adeeb SaitTawfik Al-HadhramiFaisal SaeedShadi BasurraSultan Noman Qasem
This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system’s potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas.
This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system’s potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas.Eliciting and modeling emotional requirements: a systematic mapping reviewhttps://peerj.com/articles/cs-17822024-01-192024-01-19Mashail N. AlkhomsanMalak BaslymanMohammad Alshayeb
Context
Considering users’ emotions plays an extremely crucial role in the adoption and acceptance of recent technology by the end user. User emotions can also help to identify unknown requirements, saving resources that would otherwise be wasted if discovered later. However, eliciting and modeling users’ emotional requirements in software engineering is still an open research area.
Objective
This systematic mapping review analyzes emotional requirements (ER) practices in software engineering from two perspectives: elicitation and modeling. For elicitation techniques, we investigate the techniques, evaluation methods, limitations, and application domains. For modeling techniques, we examine the modeling languages, analyses, limitations, and domains.
Method
We systematically reviewed studies on emotional requirements engineering published between 1993–2023 and identified 46 relevant primary studies.
Results
A total of 34 studies investigated ER elicitation techniques, five examined modeling techniques, and seven covered both. Illustrative case studies were the main evaluation method for proposed elicitation techniques. Identified limitations include time consumption and extensive human involvement. The dominant application domains were healthcare and well-being, and game development.
Conclusion
This review summarizes the current landscape of emotional requirements research, highlighting key elicitation and modeling techniques, evaluations, limitations, and domains. Further research can build on these findings to advance emotional requirements practices in software engineering. Future research may address (1) managing conflicting emotional requirements across users, (2) evaluating the value and impact of considering emotional requirements during the development and (3) Modeling and analyzing emotional requirements in relation to other requirements.
Context
Considering users’ emotions plays an extremely crucial role in the adoption and acceptance of recent technology by the end user. User emotions can also help to identify unknown requirements, saving resources that would otherwise be wasted if discovered later. However, eliciting and modeling users’ emotional requirements in software engineering is still an open research area.
Objective
This systematic mapping review analyzes emotional requirements (ER) practices in software engineering from two perspectives: elicitation and modeling. For elicitation techniques, we investigate the techniques, evaluation methods, limitations, and application domains. For modeling techniques, we examine the modeling languages, analyses, limitations, and domains.
Method
We systematically reviewed studies on emotional requirements engineering published between 1993–2023 and identified 46 relevant primary studies.
Results
A total of 34 studies investigated ER elicitation techniques, five examined modeling techniques, and seven covered both. Illustrative case studies were the main evaluation method for proposed elicitation techniques. Identified limitations include time consumption and extensive human involvement. The dominant application domains were healthcare and well-being, and game development.
Conclusion
This review summarizes the current landscape of emotional requirements research, highlighting key elicitation and modeling techniques, evaluations, limitations, and domains. Further research can build on these findings to advance emotional requirements practices in software engineering. Future research may address (1) managing conflicting emotional requirements across users, (2) evaluating the value and impact of considering emotional requirements during the development and (3) Modeling and analyzing emotional requirements in relation to other requirements.Cluster and trajectory analysis of motivation in an emergency remote programming coursehttps://peerj.com/articles/cs-17872024-01-122024-01-12Andres JahrJaviera MezaJorge Munoz-GamaLuis HerskovicValeria Herskovic
Emergency remote teaching is a temporary change in the way education occurs, whereby an educational system unexpectedly becomes entirely remote. This article analyzes the motivation of students undertaking a university course over one semester of emergency remote teaching in the context of the COVID-19 pandemic. University students undertaking a programming course were surveyed three times during one semester, about motivation and COVID concern. This work explores which student motivation profiles existed, how motivation evolved, and whether concern about the pandemic was a factor affecting motivation throughout the course. The most adaptive profile was highly motivated, more prepared and less frustrated by the conditions of the course. However, this cluster experienced the highest levels of COVID-19 concern. The least adaptive cluster behaved as a mirror image of the most adaptive cluster. Clear differences were found between the clusters that showed the most and least concern about COVID-19.
Emergency remote teaching is a temporary change in the way education occurs, whereby an educational system unexpectedly becomes entirely remote. This article analyzes the motivation of students undertaking a university course over one semester of emergency remote teaching in the context of the COVID-19 pandemic. University students undertaking a programming course were surveyed three times during one semester, about motivation and COVID concern. This work explores which student motivation profiles existed, how motivation evolved, and whether concern about the pandemic was a factor affecting motivation throughout the course. The most adaptive profile was highly motivated, more prepared and less frustrated by the conditions of the course. However, this cluster experienced the highest levels of COVID-19 concern. The least adaptive cluster behaved as a mirror image of the most adaptive cluster. Clear differences were found between the clusters that showed the most and least concern about COVID-19.Attention-enhanced gated recurrent unit for action recognition in tennishttps://peerj.com/articles/cs-18042024-01-112024-01-11Meng GaoBingchun Ju
Human Action Recognition (HAR) is an essential topic in computer vision and artificial intelligence, focused on the automatic identification and categorization of human actions or activities from video sequences or sensor data. The goal of HAR is to teach machines to comprehend and interpret human movements, gestures, and behaviors, allowing for a wide range of applications in areas such as surveillance, healthcare, sports analysis, and human-computer interaction. HAR systems utilize a variety of techniques, including deep learning, motion analysis, and feature extraction, to capture and analyze the spatiotemporal characteristics of human actions. These systems have the capacity to distinguish between various actions, whether they are simple actions like walking and waving or more complex activities such as playing a musical instrument or performing sports maneuvers. HAR continues to be an active area of research and development, with the potential to enhance numerous real-world applications by providing machines with the ability to understand and respond to human actions effectively. In our study, we developed a HAR system to recognize actions in tennis using an attention-based gated recurrent unit (GRU), a prevalent recurrent neural network. The combination of GRU architecture and attention mechanism showed a significant improvement in prediction power compared to two other deep learning models. Our models were trained on the THETIS dataset, one of the standard medium-sized datasets for fine-grained tennis actions. The effectiveness of the proposed model was confirmed by three different types of image encoders: InceptionV3, DenseNet, and EfficientNetB5. The models developed with InceptionV3, DenseNet, and EfficientNetB5 achieved average ROC-AUC values of 0.97, 0.98, and 0.81, respectively. While, the models obtained average PR-AUC values of 0.84, 0.87, and 0.49 for InceptionV3, DenseNet, and EfficientNetB5 features, respectively. The experimental results confirmed the applicability of our proposed method in recognizing action in tennis and may be applied to other HAR problems.
Human Action Recognition (HAR) is an essential topic in computer vision and artificial intelligence, focused on the automatic identification and categorization of human actions or activities from video sequences or sensor data. The goal of HAR is to teach machines to comprehend and interpret human movements, gestures, and behaviors, allowing for a wide range of applications in areas such as surveillance, healthcare, sports analysis, and human-computer interaction. HAR systems utilize a variety of techniques, including deep learning, motion analysis, and feature extraction, to capture and analyze the spatiotemporal characteristics of human actions. These systems have the capacity to distinguish between various actions, whether they are simple actions like walking and waving or more complex activities such as playing a musical instrument or performing sports maneuvers. HAR continues to be an active area of research and development, with the potential to enhance numerous real-world applications by providing machines with the ability to understand and respond to human actions effectively. In our study, we developed a HAR system to recognize actions in tennis using an attention-based gated recurrent unit (GRU), a prevalent recurrent neural network. The combination of GRU architecture and attention mechanism showed a significant improvement in prediction power compared to two other deep learning models. Our models were trained on the THETIS dataset, one of the standard medium-sized datasets for fine-grained tennis actions. The effectiveness of the proposed model was confirmed by three different types of image encoders: InceptionV3, DenseNet, and EfficientNetB5. The models developed with InceptionV3, DenseNet, and EfficientNetB5 achieved average ROC-AUC values of 0.97, 0.98, and 0.81, respectively. While, the models obtained average PR-AUC values of 0.84, 0.87, and 0.49 for InceptionV3, DenseNet, and EfficientNetB5 features, respectively. The experimental results confirmed the applicability of our proposed method in recognizing action in tennis and may be applied to other HAR problems.