In recent years, using online has emerged as the predominant method to book an airline ticket. Following the COVID-19 pandemic, there has been a surge in the popularity of small-group travel in Japan, leading to an increasing number of smartphone users booking tickets. Consequently, the user-friendliness of airline applications profoundly impacts users' willingness to make purchases. This study aimed to visualize challenges and areas for improvement in airline ticket reservations focused on Japan Airlines and Thai Airways. Therefore, experimental investigations were conducted using these smartphone applications of two airlines. The experiments involved five participants who were tasked with navigating through the entire reservation process using the applications of the respective airlines. Subsequently, we gathered participants' impressions regarding their experience of booking a flight ticket with the retrospective protocol analysis as a qualitative method. The flow of the reservation process was primarily categorized into the following five segments: location selection, date selection, flight selection, and personal information input.The results from the experiment underscore the essential attributes of a user-friendly application for booking a flight ticket. These attributes include optimizing the presentation of information, effectively categorizing data, strategically placing buttons to minimize errors, and ensuring a consistent navigational experience. This study's outcomes highlight that enhancing the usability of applications requires deliberate attention to these factors. In conclusion, this study addresses the pressing concern of designing intuitive and user-friendly airline applications for booking a flight ticket. This study also effectively categorizes the reservation process into key stages by focusing on the applications of Japan Airlines and Thai Airways. This comprehensive analysis accentuates the importance of design considerations in promoting user satisfaction, enabling airlines to cater to the growing trend of online reservations and offer users a seamless experience for booking a flight ticket.
{"title":"Usability of Booking a Flight Ticket Using Airline Applications on Smartphones","authors":"Emiri Otsuka, Namgyu Kang","doi":"10.54941/ahfe1004245","DOIUrl":"https://doi.org/10.54941/ahfe1004245","url":null,"abstract":"In recent years, using online has emerged as the predominant method to book an airline ticket. Following the COVID-19 pandemic, there has been a surge in the popularity of small-group travel in Japan, leading to an increasing number of smartphone users booking tickets. Consequently, the user-friendliness of airline applications profoundly impacts users' willingness to make purchases. This study aimed to visualize challenges and areas for improvement in airline ticket reservations focused on Japan Airlines and Thai Airways. Therefore, experimental investigations were conducted using these smartphone applications of two airlines. The experiments involved five participants who were tasked with navigating through the entire reservation process using the applications of the respective airlines. Subsequently, we gathered participants' impressions regarding their experience of booking a flight ticket with the retrospective protocol analysis as a qualitative method. The flow of the reservation process was primarily categorized into the following five segments: location selection, date selection, flight selection, and personal information input.The results from the experiment underscore the essential attributes of a user-friendly application for booking a flight ticket. These attributes include optimizing the presentation of information, effectively categorizing data, strategically placing buttons to minimize errors, and ensuring a consistent navigational experience. This study's outcomes highlight that enhancing the usability of applications requires deliberate attention to these factors. In conclusion, this study addresses the pressing concern of designing intuitive and user-friendly airline applications for booking a flight ticket. This study also effectively categorizes the reservation process into key stages by focusing on the applications of Japan Airlines and Thai Airways. This comprehensive analysis accentuates the importance of design considerations in promoting user satisfaction, enabling airlines to cater to the growing trend of online reservations and offer users a seamless experience for booking a flight ticket.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260748","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}
Elizabeth Wianto, Hapnes Toba, Chien-Hsu Chen, Maya Malinda
Maintaining a high quality of life through physical activities (PA) to prevent health decline is crucial. However, the relationship between individuals' health status, PA preferences, and motion factors is complex. PA discussions consistently show a positive correlation with healthy aging experiences, but no explicit relation to specific types of musculoskeletal exercises. Taking advantage of the increasingly widespread existence of smartphones, especially in Indonesia, this research utilizes embedded sensors for Human Activity Recognition (HAR). Based on 25 participants' data, performing nine types of selected motion, this study has successfully identified important sensor attributes that play important roles in the right and left hands for muscle strength motions as the basis for developing machine learning models with the LSTM algorithm.
{"title":"Sensor-based Data Acquisition via Ubiquitous Device to Detect Muscle Strength Training Activities","authors":"Elizabeth Wianto, Hapnes Toba, Chien-Hsu Chen, Maya Malinda","doi":"10.54941/ahfe1004213","DOIUrl":"https://doi.org/10.54941/ahfe1004213","url":null,"abstract":"Maintaining a high quality of life through physical activities (PA) to prevent health decline is crucial. However, the relationship between individuals' health status, PA preferences, and motion factors is complex. PA discussions consistently show a positive correlation with healthy aging experiences, but no explicit relation to specific types of musculoskeletal exercises. Taking advantage of the increasingly widespread existence of smartphones, especially in Indonesia, this research utilizes embedded sensors for Human Activity Recognition (HAR). Based on 25 participants' data, performing nine types of selected motion, this study has successfully identified important sensor attributes that play important roles in the right and left hands for muscle strength motions as the basis for developing machine learning models with the LSTM algorithm.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260765","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}
Urban Air Mobility (UAM) era is predicted to arrive, with the commercialization of Personal Air Vehicle (PAV) expected by 2025. The cabin design direction of traditional aircraft and PAV differs significantly, and considering the perspective of a new space that users have not previously experienced, it is necessary to understand user perceptions in order to provide an environment that ensures satisfaction, comfort, and stability. Furthermore, while the cockpit in traditional aircraft is separated and disconnected, PAV may have direct interaction points between pilots and passengers, necessitating consideration of social factors related to pilot-passenger interaction. The purpose of this study is to identify the physical and social servicescape factors in PAV cabins that may influence the experiences of passengers and pilots. According to the survey results, both pilots and passengers rated the "Safety in Emergencies" element as the most important. It takes into account the confined interior of the cabin, examining environmental factors that impact not only passengers but also pilots, thus providing a holistic understanding and presentation of the overall aspects.
{"title":"Considerations for Cabin Design in Urban Air Mobility's Personal Air Vehicles with a Focus on User Experience","authors":"Ju Yeong Kwon, Seok Jun Jin, Da young Ju","doi":"10.54941/ahfe1004261","DOIUrl":"https://doi.org/10.54941/ahfe1004261","url":null,"abstract":"Urban Air Mobility (UAM) era is predicted to arrive, with the commercialization of Personal Air Vehicle (PAV) expected by 2025. The cabin design direction of traditional aircraft and PAV differs significantly, and considering the perspective of a new space that users have not previously experienced, it is necessary to understand user perceptions in order to provide an environment that ensures satisfaction, comfort, and stability. Furthermore, while the cockpit in traditional aircraft is separated and disconnected, PAV may have direct interaction points between pilots and passengers, necessitating consideration of social factors related to pilot-passenger interaction. The purpose of this study is to identify the physical and social servicescape factors in PAV cabins that may influence the experiences of passengers and pilots. According to the survey results, both pilots and passengers rated the \"Safety in Emergencies\" element as the most important. It takes into account the confined interior of the cabin, examining environmental factors that impact not only passengers but also pilots, thus providing a holistic understanding and presentation of the overall aspects.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135261506","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}
In recent years, the accuracy of speech recognition has improved remarkably. Speech recognition software can be used to obtain text information from conversational speech data. Although text can be treated as surface level information, several studies have indicated that speech recognition can also be used to estimate emotions, which represent higher level information in a conversation. Several newly proposed models use LSTM or GRU to estimate emotion in conversations. However, when attempting to monitor or influence conversations conducted as part of a meeting or a chat, the mood of the conversation is more important than the emotion. In normal conversation, emotions such as anger and sadness are unlikely to be explicitly expressed for some purposes, including avoidance of getting into an unexpected argument and offending others. Thus, when attempting to control or monitor the state of a conversation during a meeting or casual discussion, it is often more important to estimate the mood than the emotion. Some researchers have examined the role of mood, as distinguished from emotion, and one called diffuse emotional states that persist over a long period of time "mood" and are usually distinguished based on duration and intensity of expression. However, these differences are rarely quantified, and no specific durations are fixed. Accurate identification of the mood of a conversation is especially important for Japanese people who are engaged in collaborative and democratic decision making. To construct the teacher data for the model designed to estimate the conversational mood, we first selected representative adjective pairs that could describe the conversational mood. We utilized a system developed by Iiba et al. to estimate 21 affective scales of adjective pairs from input text. The 21 adjective pairs were clustered into 4 groups based on the output scales. The 4 adjective pairs to be annotated were representative of the 4 clusters. We expected these 4 adjective pairs (gloomy-happy, easy-serious, calm-aggressive, tidy-messy) to capture the mood of a conversation.Based on the four adjective pairs, we constructed a new training data set containing 60 hours of conversations in Japanese. In this study, the data obtained only by microphones are used for estimation of conversational mood. The data set was annotated by the four adjective scales to learn the mood of the conversations. We de-veloped a LSTM deep neural network model that could read the "conversational mood" in real time. Furthermore, in our proposed neural network model, the amount of laughter which is generally measured by capturing facial expression with camera is also estimated together with the conversational mood. Because laughter is considered to play an important role in creating a cheerful environment, it can be used to evaluate the conversational mood. The evaluation results are shown to present the validity of our model. This model is expected to be applied to a system that can
{"title":"Neural Network Model for Visualization of Conversational Mood with Four Adjective Pairs","authors":"Koichi Yamagata, Koya Kawahara, Yuto Suzuki, Yuki Nakahodo, Shunsuke Ito, Haruka Matsukura, Maki Sakamoto","doi":"10.54941/ahfe1004396","DOIUrl":"https://doi.org/10.54941/ahfe1004396","url":null,"abstract":"In recent years, the accuracy of speech recognition has improved remarkably. Speech recognition software can be used to obtain text information from conversational speech data. Although text can be treated as surface level information, several studies have indicated that speech recognition can also be used to estimate emotions, which represent higher level information in a conversation. Several newly proposed models use LSTM or GRU to estimate emotion in conversations. However, when attempting to monitor or influence conversations conducted as part of a meeting or a chat, the mood of the conversation is more important than the emotion. In normal conversation, emotions such as anger and sadness are unlikely to be explicitly expressed for some purposes, including avoidance of getting into an unexpected argument and offending others. Thus, when attempting to control or monitor the state of a conversation during a meeting or casual discussion, it is often more important to estimate the mood than the emotion. Some researchers have examined the role of mood, as distinguished from emotion, and one called diffuse emotional states that persist over a long period of time \"mood\" and are usually distinguished based on duration and intensity of expression. However, these differences are rarely quantified, and no specific durations are fixed. Accurate identification of the mood of a conversation is especially important for Japanese people who are engaged in collaborative and democratic decision making. To construct the teacher data for the model designed to estimate the conversational mood, we first selected representative adjective pairs that could describe the conversational mood. We utilized a system developed by Iiba et al. to estimate 21 affective scales of adjective pairs from input text. The 21 adjective pairs were clustered into 4 groups based on the output scales. The 4 adjective pairs to be annotated were representative of the 4 clusters. We expected these 4 adjective pairs (gloomy-happy, easy-serious, calm-aggressive, tidy-messy) to capture the mood of a conversation.Based on the four adjective pairs, we constructed a new training data set containing 60 hours of conversations in Japanese. In this study, the data obtained only by microphones are used for estimation of conversational mood. The data set was annotated by the four adjective scales to learn the mood of the conversations. We de-veloped a LSTM deep neural network model that could read the \"conversational mood\" in real time. Furthermore, in our proposed neural network model, the amount of laughter which is generally measured by capturing facial expression with camera is also estimated together with the conversational mood. Because laughter is considered to play an important role in creating a cheerful environment, it can be used to evaluate the conversational mood. The evaluation results are shown to present the validity of our model. This model is expected to be applied to a system that can","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262493","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}
In eddy current testing, it is desirable to keep sensor perpendicular to test surface, but it is difficult to automatically determine the sensor posture at inspection points with complex geometry, and the non-destructive testing technician manually operates the sensor. In such cases, it is necessary to ensure skill of the technicians as they are part of the non-destructive testing system. We are conducting research to establish a skills training method to efficiently develop non-destructive testing technicians. The behavior of licensed and unlicensed subjects was measured while inspecting defects around bolt holes. There was a clear statistical difference between the sequences of licensed and unlicensed subjects.ts will be established, and a prototype real-time skill teaching system will be built to verify the validity of the proposed method.
{"title":"Quantitative Assessment of Eddy Current Inspection Technician Skills","authors":"Daigo Kosaka, Masahiro Hoshiba, Hiroyuki Nakamoto","doi":"10.54941/ahfe1004427","DOIUrl":"https://doi.org/10.54941/ahfe1004427","url":null,"abstract":"In eddy current testing, it is desirable to keep sensor perpendicular to test surface, but it is difficult to automatically determine the sensor posture at inspection points with complex geometry, and the non-destructive testing technician manually operates the sensor. In such cases, it is necessary to ensure skill of the technicians as they are part of the non-destructive testing system. We are conducting research to establish a skills training method to efficiently develop non-destructive testing technicians. The behavior of licensed and unlicensed subjects was measured while inspecting defects around bolt holes. There was a clear statistical difference between the sequences of licensed and unlicensed subjects.ts will be established, and a prototype real-time skill teaching system will be built to verify the validity of the proposed method.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262496","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}
Ramona Schmid, Sophia Maria Saat, Knut Möller, Verena Wagner-Hartl
Recognizing emotions is an essential ability in our daily social interactions. However, there are individuals who have difficulties interpreting emotions, such as patients with autism spectrum disorders (ASD). In order to cope better with everyday life, emotion training can be a supporting factor for them. However, studies show that emotion training is not only helpful for patients with ASD, but also in the working environment, for example in trainings for managers or teams. In recent research, there are already approaches to use new technologies such as virtual reality to train emotional and social skills. For the evaluation of these new concepts, it is important to make the emotional state of a person measurable. Therefore, a measurement environment has already been developed at Furtwangen University. This is based on a multidimensional approach combining subjective and objective psychophysiological measures. Moreover, the development of facial emotion recognition (FER) systems based on machine learning techniques are also increasing for measuring a person's emotional state. Often, they focus on the recognition of Ekman’s basic emotions. To train and evaluate such FER systems, these basic emotions have to be induced in an individual. Therefore, a number of methods for emotion induction can be found in research, e.g. visual stimuli or mental methods. However, in most studies, only a few selected emotions, such as anger and happiness, were induced. Thus, there is a lack of studies that examined the induction of all six basic emotions.For that reason, the aim of the presented experimental study was to investigate two different methods of emotion induction for the six basic emotions anger, disgust, fear, happiness, sadness, surprise, and a neutral category. Overall, 14 women and 10 men (N = 24) aged between 19 and 59 years (M = 29.25, SD = 11.46) participated in the study. For the first induction method, affective visual stimuli from common emotional picture databases (EmoPicS, OASIS and IAPS) were used. For the second induction method, emotions were induced by a so-called autobiographical recall. Therefore, the participants had to imagine autobiographical situations that evoked the required emotion in them in the past. After each different induction of one of the six emotions or the neutral category, the participants’ emotional state was assessed using the two dimensions valence and arousal of the Self-Assessment Manikin (SAM). Furthermore, cardiovascular (ECG) and electrodermal (EDA) activity were recorded. The results show a significant interaction induction method x emotional category for both subjective assessments valence and arousal. Furthermore, based on the results of the psychophysiological responses of the participants (ECG and EDA), it is shown that the second method to induce emotions (autobiographical recall) was significantly more arousing than the first induction method using visual stimuli. To sum it up, the results of the experime
{"title":"Induction method influence on emotion recognition based on psychophysiological parameters","authors":"Ramona Schmid, Sophia Maria Saat, Knut Möller, Verena Wagner-Hartl","doi":"10.54941/ahfe1002851","DOIUrl":"https://doi.org/10.54941/ahfe1002851","url":null,"abstract":"Recognizing emotions is an essential ability in our daily social interactions. However, there are individuals who have difficulties interpreting emotions, such as patients with autism spectrum disorders (ASD). In order to cope better with everyday life, emotion training can be a supporting factor for them. However, studies show that emotion training is not only helpful for patients with ASD, but also in the working environment, for example in trainings for managers or teams. In recent research, there are already approaches to use new technologies such as virtual reality to train emotional and social skills. For the evaluation of these new concepts, it is important to make the emotional state of a person measurable. Therefore, a measurement environment has already been developed at Furtwangen University. This is based on a multidimensional approach combining subjective and objective psychophysiological measures. Moreover, the development of facial emotion recognition (FER) systems based on machine learning techniques are also increasing for measuring a person's emotional state. Often, they focus on the recognition of Ekman’s basic emotions. To train and evaluate such FER systems, these basic emotions have to be induced in an individual. Therefore, a number of methods for emotion induction can be found in research, e.g. visual stimuli or mental methods. However, in most studies, only a few selected emotions, such as anger and happiness, were induced. Thus, there is a lack of studies that examined the induction of all six basic emotions.For that reason, the aim of the presented experimental study was to investigate two different methods of emotion induction for the six basic emotions anger, disgust, fear, happiness, sadness, surprise, and a neutral category. Overall, 14 women and 10 men (N = 24) aged between 19 and 59 years (M = 29.25, SD = 11.46) participated in the study. For the first induction method, affective visual stimuli from common emotional picture databases (EmoPicS, OASIS and IAPS) were used. For the second induction method, emotions were induced by a so-called autobiographical recall. Therefore, the participants had to imagine autobiographical situations that evoked the required emotion in them in the past. After each different induction of one of the six emotions or the neutral category, the participants’ emotional state was assessed using the two dimensions valence and arousal of the Self-Assessment Manikin (SAM). Furthermore, cardiovascular (ECG) and electrodermal (EDA) activity were recorded. The results show a significant interaction induction method x emotional category for both subjective assessments valence and arousal. Furthermore, based on the results of the psychophysiological responses of the participants (ECG and EDA), it is shown that the second method to induce emotions (autobiographical recall) was significantly more arousing than the first induction method using visual stimuli. To sum it up, the results of the experime","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470408","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}
This article attempts to study the consistency, among other auxiliary comparisons, between popular generative artificial intelligence (AI) robots in the evaluation of various perceived user experience dimensions of mobile device operating system versions or, more specifically, iOS and Android versions. A handful of robots were experimented with, ending up with Dragonfly and GPT-4 being the only two eligible for in-depth investigation where the duo was individually requested to accord rating scores to the six major dimensions, namely (1) efficiency, (2) effectiveness, (3) learnability, (4) satisfaction, (5) accessibility, and (6) security, of the operating system versions. It is noteworthy that these dimensions are from the perceived user experience’s point of view instead of any “physical” technology’s standpoint. For each of the two robots, the minimum, the maximum, the range, and the standard deviation of the rating scores for each of the six dimensions were computed across all the versions. The rating score difference for each of the six dimensions between the two robots was calculated for each version. The mean of the absolute value, the minimum, the maximum, the range, and the standard deviation of the differences for each dimension between the two robots were calculated across all versions. A paired sample t-test was then applied to each dimension for the rating score differences between the two robots over all the versions. Finally, a correlation coefficient of the rating scores was computed for each dimension between the two robots across all the versions. These computational outcomes were to confirm whether the two robots awarded discrimination in evaluating each dimension across the versions, whether any of the two robots systematically underrated or overrated any dimension vis-à-vis the other robot, and whether there was consistency between the two robots in evaluating each dimension across the versions. It was found that discrimination was apparent in the evaluation of all dimensions, GPT-4 systematically underrated the dimensions satisfaction (p = 0.002 < 0.05) and security (p = 0.008 < 0.05) compared with Dragonfly, and the evaluation by the two robots was almost impeccably consistent for the six dimensions with the correlation coefficients ranging from 0.679 to 0.892 (p from 0.000 to 0.003 < 0.05). Consistency implies at least the partial trustworthiness of the evaluation of these mobile device operating system versions by either of these two popular generative AI robots based on the analogous concept of convergent validity.
{"title":"The Consistency between Popular Generative Artificial Intelligence (AI) Robots in Evaluating the User Experience of Mobile Device Operating Systems","authors":"Victor K Y Chan","doi":"10.54941/ahfe1004193","DOIUrl":"https://doi.org/10.54941/ahfe1004193","url":null,"abstract":"This article attempts to study the consistency, among other auxiliary comparisons, between popular generative artificial intelligence (AI) robots in the evaluation of various perceived user experience dimensions of mobile device operating system versions or, more specifically, iOS and Android versions. A handful of robots were experimented with, ending up with Dragonfly and GPT-4 being the only two eligible for in-depth investigation where the duo was individually requested to accord rating scores to the six major dimensions, namely (1) efficiency, (2) effectiveness, (3) learnability, (4) satisfaction, (5) accessibility, and (6) security, of the operating system versions. It is noteworthy that these dimensions are from the perceived user experience’s point of view instead of any “physical” technology’s standpoint. For each of the two robots, the minimum, the maximum, the range, and the standard deviation of the rating scores for each of the six dimensions were computed across all the versions. The rating score difference for each of the six dimensions between the two robots was calculated for each version. The mean of the absolute value, the minimum, the maximum, the range, and the standard deviation of the differences for each dimension between the two robots were calculated across all versions. A paired sample t-test was then applied to each dimension for the rating score differences between the two robots over all the versions. Finally, a correlation coefficient of the rating scores was computed for each dimension between the two robots across all the versions. These computational outcomes were to confirm whether the two robots awarded discrimination in evaluating each dimension across the versions, whether any of the two robots systematically underrated or overrated any dimension vis-à-vis the other robot, and whether there was consistency between the two robots in evaluating each dimension across the versions. It was found that discrimination was apparent in the evaluation of all dimensions, GPT-4 systematically underrated the dimensions satisfaction (p = 0.002 < 0.05) and security (p = 0.008 < 0.05) compared with Dragonfly, and the evaluation by the two robots was almost impeccably consistent for the six dimensions with the correlation coefficients ranging from 0.679 to 0.892 (p from 0.000 to 0.003 < 0.05). Consistency implies at least the partial trustworthiness of the evaluation of these mobile device operating system versions by either of these two popular generative AI robots based on the analogous concept of convergent validity.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135314216","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}
Michelle Stewart, Andrea Tineo, Benjamin Woodrow, Michael Wasik, Selina Chan
Exposure to extreme heat during physical exertion may impair cognitive and physical abilities commonly known as heat stress. Industrial workers are vulnerable to the effects of extreme heat due to increasing ambient temperatures, tasks with radiant heat exposures, work intensity, and added personal protective equipment (PPE) burden. New wearable sweat sensors may help mitigate heat stress by monitoring physiological signs of dehydration and provide real-time hydration recommendations. As wearable sensors are introduced into the workplace, this study aims to understand whether continuous personal, physiological monitoring is a better indicator of heat stress risk than current, traditional industrial hygiene, environmental monitoring.
{"title":"Continuous personal monitoring and personalized hydration recommendations with wearable sweat sensors to prevent occupational heat stress","authors":"Michelle Stewart, Andrea Tineo, Benjamin Woodrow, Michael Wasik, Selina Chan","doi":"10.54941/ahfe1004205","DOIUrl":"https://doi.org/10.54941/ahfe1004205","url":null,"abstract":"Exposure to extreme heat during physical exertion may impair cognitive and physical abilities commonly known as heat stress. Industrial workers are vulnerable to the effects of extreme heat due to increasing ambient temperatures, tasks with radiant heat exposures, work intensity, and added personal protective equipment (PPE) burden. New wearable sweat sensors may help mitigate heat stress by monitoring physiological signs of dehydration and provide real-time hydration recommendations. As wearable sensors are introduced into the workplace, this study aims to understand whether continuous personal, physiological monitoring is a better indicator of heat stress risk than current, traditional industrial hygiene, environmental monitoring.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316839","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}
Nowadays the rapid advancements in artificial intelligence (AI) have significantly transformed various industries, including the business ecosystem (Agrawal, A., Gans, J., & Goldfarb, A., 2022). This study aims to examine the multifaceted impact of AI on business ecosystem development, considering both the positive and negative aspects mostly focused on developed countries.The positive effects of AI implementation on the business ecosystem are manifold. AI-powered technologies enhance productivity and efficiency, automate repetitive tasks, and optimize resource allocation(Floridi, L., 2019). Furthermore, AI algorithms enable businesses to gain valuable insights from large volumes of data, leading to improved decision-making processes and the identification of new market trends (Martin, R., & McCrae, D., 2020). However, along with the promising prospects, there are notable concerns surrounding the implementation of AI in the business ecosystem. Ethical issues, such as privacy infringement and data security, arise due to the vast amounts of sensitive information processed by AI systems. (Davenport, T. H., & Ronanki, R. (2018). Furthermore, the concentration of power in AI technologies within a few dominant players can lead to challenges related to market competition and access to AI-driven solutions.This study combines a comprehensive review of existing literature with case studies and expert interviews to provide a balanced assessment of the impact of AI on business ecosystem development. By analyzing real-world examples and industry cases, this research aims to shed light on the practical implications of AI implementation and identify strategies to mitigate potential risks and challenges.The findings of this study will contribute to the ongoing discussions surrounding the integration of AI technologies in the business ecosystem. The results will be of interest to policymakers, business leaders, and researchers, providing valuable insights into harnessing the potential benefits of AI while addressing the associated concerns.
{"title":"The impact of AI on business ecosystem development: pro and contra","authors":"Olga Shvetsova","doi":"10.54941/ahfe1004425","DOIUrl":"https://doi.org/10.54941/ahfe1004425","url":null,"abstract":"Nowadays the rapid advancements in artificial intelligence (AI) have significantly transformed various industries, including the business ecosystem (Agrawal, A., Gans, J., & Goldfarb, A., 2022). This study aims to examine the multifaceted impact of AI on business ecosystem development, considering both the positive and negative aspects mostly focused on developed countries.The positive effects of AI implementation on the business ecosystem are manifold. AI-powered technologies enhance productivity and efficiency, automate repetitive tasks, and optimize resource allocation(Floridi, L., 2019). Furthermore, AI algorithms enable businesses to gain valuable insights from large volumes of data, leading to improved decision-making processes and the identification of new market trends (Martin, R., & McCrae, D., 2020). However, along with the promising prospects, there are notable concerns surrounding the implementation of AI in the business ecosystem. Ethical issues, such as privacy infringement and data security, arise due to the vast amounts of sensitive information processed by AI systems. (Davenport, T. H., & Ronanki, R. (2018). Furthermore, the concentration of power in AI technologies within a few dominant players can lead to challenges related to market competition and access to AI-driven solutions.This study combines a comprehensive review of existing literature with case studies and expert interviews to provide a balanced assessment of the impact of AI on business ecosystem development. By analyzing real-world examples and industry cases, this research aims to shed light on the practical implications of AI implementation and identify strategies to mitigate potential risks and challenges.The findings of this study will contribute to the ongoing discussions surrounding the integration of AI technologies in the business ecosystem. The results will be of interest to policymakers, business leaders, and researchers, providing valuable insights into harnessing the potential benefits of AI while addressing the associated concerns.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135317060","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}
Wang Jianlun, Deng Huangtianci, Su Rina, Can He, Han Yu, He Jianlei, Hu Baoyue, Chen Husheng, Huang Sheng, Xiao Sirong, Cao Jinduo
Agricultural operations require simple, efficient and robust measurement method of three dimensional forms for the plant organs such as leaves to analyse other kinds of phenotype in detail on this basis. However, most of the existing sample-based methods reconstruct three dimensional shapes of the images for the objects of smooth surface and homogeneous materials, such as plastics, paints, ceramics, and metals, etc., rather than for the natural objects of convex-concave surfaces and varying albedo materials under the arbitrary natural lights. In this paper, it was found that the methods based on the prior model with photometric stereo superposed BRDF proposed can accurately realize the 3D modelling for plant leaf image and may reduce the cumulative error. With the differential gradient constraint and integral gradient constraint proposed, the unique solution for the normal vectors of all micro panels of the pixel projection on the leaf surface was matched by the first-order central difference equation and the iterations, and this process solved the ill-posed problem of BRDF. The experiment results showed that the average error between the height reconstructed results and the measured results of the real leaves’ height was 15% and the attenuation error was reduced by our method.
{"title":"A sample-based method of 3D reconstruction for plant leaf from single image or multiple images","authors":"Wang Jianlun, Deng Huangtianci, Su Rina, Can He, Han Yu, He Jianlei, Hu Baoyue, Chen Husheng, Huang Sheng, Xiao Sirong, Cao Jinduo","doi":"10.54941/ahfe1004195","DOIUrl":"https://doi.org/10.54941/ahfe1004195","url":null,"abstract":"Agricultural operations require simple, efficient and robust measurement method of three dimensional forms for the plant organs such as leaves to analyse other kinds of phenotype in detail on this basis. However, most of the existing sample-based methods reconstruct three dimensional shapes of the images for the objects of smooth surface and homogeneous materials, such as plastics, paints, ceramics, and metals, etc., rather than for the natural objects of convex-concave surfaces and varying albedo materials under the arbitrary natural lights. In this paper, it was found that the methods based on the prior model with photometric stereo superposed BRDF proposed can accurately realize the 3D modelling for plant leaf image and may reduce the cumulative error. With the differential gradient constraint and integral gradient constraint proposed, the unique solution for the normal vectors of all micro panels of the pixel projection on the leaf surface was matched by the first-order central difference equation and the iterations, and this process solved the ill-posed problem of BRDF. The experiment results showed that the average error between the height reconstructed results and the measured results of the real leaves’ height was 15% and the attenuation error was reduced by our method.","PeriodicalId":470195,"journal":{"name":"AHFE international","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318030","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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