Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers最新文献
For avoiding excessive congestion of tourists that causes overtourism, we propose a Generic Point of Interest (POI), which is an alternative sightseeing spot potentially attractive enough for tourists to replace a well-known sightseeing spot. We also propose a method to discover generic POIs and evaluate it. While the rapid spread of social networking services (SNSs) and social media makes tourism more familiar to people, it is further aggravating overtoursim around the world due to the nature of SNSs and social media, where users simultaneously find the same posts or articles recommending specific tourist spots and are attracted to the same destinations at the same time. As overtourism has severe influences on both visitors and local residents, it is essential to solve this problem. Although there are many studies providing ways of recommending less crowded tourist spots or mining less-known spots in a famous sightseeing area, we cannot apply those methods as a fundamental solution for overtourism for two reasons: 1) in many cases, the number of tourists already exceeds the touring area's total capacity; and 2) many approaches relying on a number of user-generated data points cannot discover unbusy sightseeing spots since users hardly post reviews nor images. To address these challenges, we propose a novel concept of generic POIs, alternative sightseeing spots to famous spots, and we propose a method to discover generic POIs, whose images are similar to those of existing famous sightseeing spots. We also evaluate our method with collected examples of generic POIs. We hope that the proposed method will help alleviate the overtourism problem in the real world as a first step.
{"title":"Generic POI recommendation","authors":"Hisao Katsumi, Wataru Yamada, Keiichi Ochiai","doi":"10.1145/3410530.3414421","DOIUrl":"https://doi.org/10.1145/3410530.3414421","url":null,"abstract":"For avoiding excessive congestion of tourists that causes overtourism, we propose a Generic Point of Interest (POI), which is an alternative sightseeing spot potentially attractive enough for tourists to replace a well-known sightseeing spot. We also propose a method to discover generic POIs and evaluate it. While the rapid spread of social networking services (SNSs) and social media makes tourism more familiar to people, it is further aggravating overtoursim around the world due to the nature of SNSs and social media, where users simultaneously find the same posts or articles recommending specific tourist spots and are attracted to the same destinations at the same time. As overtourism has severe influences on both visitors and local residents, it is essential to solve this problem. Although there are many studies providing ways of recommending less crowded tourist spots or mining less-known spots in a famous sightseeing area, we cannot apply those methods as a fundamental solution for overtourism for two reasons: 1) in many cases, the number of tourists already exceeds the touring area's total capacity; and 2) many approaches relying on a number of user-generated data points cannot discover unbusy sightseeing spots since users hardly post reviews nor images. To address these challenges, we propose a novel concept of generic POIs, alternative sightseeing spots to famous spots, and we propose a method to discover generic POIs, whose images are similar to those of existing famous sightseeing spots. We also evaluate our method with collected examples of generic POIs. We hope that the proposed method will help alleviate the overtourism problem in the real world as a first step.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84481485","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}
P. Chlebek, Elizabeth Shriberg, Yang Lu, T. Rutowski, A. Harati, R. Oliveira
Behavioral health conditions such as depression and anxiety are a global concern, and there is growing interest in employing speech technology to screen and monitor patients remotely. Language modeling approaches require automatic speech recognition (ASR) and multiple privacy-compliant ASR services are commercially available. We use a corpus of over 60 hours of speech from a behavioral health task, and compare ASR performance for four commercial vendors. We expected similar performance, but found large differences between the top and next-best performer, for both mobile (48% relative WER increase) and laptop (67% relative WER increase) data. Results suggest the importance of benchmarking ASR systems in this domain. Additionally we find that WER is not systematically related to depression itself. Performance is however affected by diverse audio quality from users' personal devices, and possibly from the overall style of speech in this domain.
{"title":"Comparing speech recognition services for HCI applications in behavioral health","authors":"P. Chlebek, Elizabeth Shriberg, Yang Lu, T. Rutowski, A. Harati, R. Oliveira","doi":"10.1145/3410530.3414372","DOIUrl":"https://doi.org/10.1145/3410530.3414372","url":null,"abstract":"Behavioral health conditions such as depression and anxiety are a global concern, and there is growing interest in employing speech technology to screen and monitor patients remotely. Language modeling approaches require automatic speech recognition (ASR) and multiple privacy-compliant ASR services are commercially available. We use a corpus of over 60 hours of speech from a behavioral health task, and compare ASR performance for four commercial vendors. We expected similar performance, but found large differences between the top and next-best performer, for both mobile (48% relative WER increase) and laptop (67% relative WER increase) data. Results suggest the importance of benchmarking ASR systems in this domain. Additionally we find that WER is not systematically related to depression itself. Performance is however affected by diverse audio quality from users' personal devices, and possibly from the overall style of speech in this domain.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"137 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79626873","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}
Accurate occupancy information is imperative for the optimization of built-in environments to achieve energy savings and user comfort. Comprehending the occupancy information provides an opportunity to interpret movement patterns, circulation-flow, space usage patterns inside the building. In this paper, we designed a case study that includes experimental testbeds from the HL Linder Hall Cafeteria; a public shared space at the University of Cincinnati College of Business, United States. Based on the time-series data collected from 3D Stereo Vision Camera, an algorithm is proposed for the removal of redundant occupant IDs to overcome inconsistencies in the Field of View (FoV) of the camera and ensure accurate estimates and consistent data. Another algorithm for data annotation in activity recognition is proposed for the binary class classification of activity with sitting and moving labels. The data obtained can be used for inspecting various types of open and shared spaces available for work and quotidian interactions among occupants. The seats and space utilization patterns extracted from the camera within the monitored area are validated using a digitally advanced tool, known as ArcGIS Pro. For the experiment, prior permission was granted by the Building Management System (BMS) authorities, and occupants' confidentiality is preserved. The space usage patterns extracted can grant access to the new dimension of investigation associated with the space selection and occupant behavior inside the buildings. This paper also discusses the challenges faced during the design phase for the deployment, and it summarizes the potential improvements in the field of occupancy sensing for energy-efficient buildings.
{"title":"Space utilization and activity recognition using 3D stereo vision camera inside an educational building","authors":"Anooshmita Das, K. Jens, M. Kjærgaard","doi":"10.1145/3410530.3414318","DOIUrl":"https://doi.org/10.1145/3410530.3414318","url":null,"abstract":"Accurate occupancy information is imperative for the optimization of built-in environments to achieve energy savings and user comfort. Comprehending the occupancy information provides an opportunity to interpret movement patterns, circulation-flow, space usage patterns inside the building. In this paper, we designed a case study that includes experimental testbeds from the HL Linder Hall Cafeteria; a public shared space at the University of Cincinnati College of Business, United States. Based on the time-series data collected from 3D Stereo Vision Camera, an algorithm is proposed for the removal of redundant occupant IDs to overcome inconsistencies in the Field of View (FoV) of the camera and ensure accurate estimates and consistent data. Another algorithm for data annotation in activity recognition is proposed for the binary class classification of activity with sitting and moving labels. The data obtained can be used for inspecting various types of open and shared spaces available for work and quotidian interactions among occupants. The seats and space utilization patterns extracted from the camera within the monitored area are validated using a digitally advanced tool, known as ArcGIS Pro. For the experiment, prior permission was granted by the Building Management System (BMS) authorities, and occupants' confidentiality is preserved. The space usage patterns extracted can grant access to the new dimension of investigation associated with the space selection and occupant behavior inside the buildings. This paper also discusses the challenges faced during the design phase for the deployment, and it summarizes the potential improvements in the field of occupancy sensing for energy-efficient buildings.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81743983","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}
Kazuya Murao, Yu Enokibori, H. Gjoreski, P. Lago, Tsuyoshi Okita, Pekka Siirtola, Kei Hiroi, P. Scholl, Mathias Ciliberto
The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Third Sussex-Huawei Locomotion and Transportation Recognition Challenge and Second Nursing Activity Recognition Challenge in special sessions.
{"title":"8th international workshop on human activity sensing corpus and applications (HASCA)","authors":"Kazuya Murao, Yu Enokibori, H. Gjoreski, P. Lago, Tsuyoshi Okita, Pekka Siirtola, Kei Hiroi, P. Scholl, Mathias Ciliberto","doi":"10.1145/3410530.3414612","DOIUrl":"https://doi.org/10.1145/3410530.3414612","url":null,"abstract":"The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Third Sussex-Huawei Locomotion and Transportation Recognition Challenge and Second Nursing Activity Recognition Challenge in special sessions.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81854370","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}
Parsa Sharmila, Vappu Schroderus, Eemil Lagerspetz, Ella Peltonen
Smartphone usage and sleep quality have established connections in psychological research, but in the HCI context, the topic is still understudied. In this paper, we present preliminary insights into behavioral patterns between smartphone usage and sleep quality by using crowdsensed data. We utilize a large-scale mobile usage dataset and a PHQ-8 depression questionnaire answered by 743 participants from varying age groups and socioeconomic backgrounds. Based on our preliminary results, we provide a methodological pipeline for future work towards understanding the relationship between daily smartphone usage patterns and sleep quality in the wild.
{"title":"Towards understanding smartphone usage and sleep with a crowdsensing approach","authors":"Parsa Sharmila, Vappu Schroderus, Eemil Lagerspetz, Ella Peltonen","doi":"10.1145/3410530.3414442","DOIUrl":"https://doi.org/10.1145/3410530.3414442","url":null,"abstract":"Smartphone usage and sleep quality have established connections in psychological research, but in the HCI context, the topic is still understudied. In this paper, we present preliminary insights into behavioral patterns between smartphone usage and sleep quality by using crowdsensed data. We utilize a large-scale mobile usage dataset and a PHQ-8 depression questionnaire answered by 743 participants from varying age groups and socioeconomic backgrounds. Based on our preliminary results, we provide a methodological pipeline for future work towards understanding the relationship between daily smartphone usage patterns and sleep quality in the wild.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83009126","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}
Ambient fine particulate (PM2.5) is the most significant risk factor for premature death, shortening life expectancy at birth by 1.5 to 1.9 years [2]. 91% of the world's population lives in areas where air pollution exceeds safety limits1. 99% of the people in countries like India, Pakistan, Nepal, and Bangladesh experience ambient exposures of PM2.5 exceeding 75 μg/m3 to 100 μg/m3 [3]. My Ph.D. thesis will be on understanding the perception of air pollution among people using social media data. I also intend to develop a wearable air pollution exposure monitor and design an air pollution visualisation tool to reduce the entry barrier for air pollution research.
{"title":"Computational tools for understanding air pollution","authors":"Rishiraj Adhikary, Nipun Batra","doi":"10.1145/3410530.3414327","DOIUrl":"https://doi.org/10.1145/3410530.3414327","url":null,"abstract":"Ambient fine particulate (PM2.5) is the most significant risk factor for premature death, shortening life expectancy at birth by 1.5 to 1.9 years [2]. 91% of the world's population lives in areas where air pollution exceeds safety limits1. 99% of the people in countries like India, Pakistan, Nepal, and Bangladesh experience ambient exposures of PM2.5 exceeding 75 μg/m3 to 100 μg/m3 [3]. My Ph.D. thesis will be on understanding the perception of air pollution among people using social media data. I also intend to develop a wearable air pollution exposure monitor and design an air pollution visualisation tool to reduce the entry barrier for air pollution research.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90066606","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}
Many current human-robot interactive systems tend to use accurate and fast - but also costly - actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.
{"title":"A low-cost prototyping framework for human-robot desk interaction","authors":"Henry Odoemelem, Kristof Van Laerhoven","doi":"10.1145/3410530.3414323","DOIUrl":"https://doi.org/10.1145/3410530.3414323","url":null,"abstract":"Many current human-robot interactive systems tend to use accurate and fast - but also costly - actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78951200","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}
Daytime sleepiness, the difficulty to maintain an alert waking state during the day, is a serious problem causing vehicle accidents and adverse effects on well-being, health, and productivity. Our research aims at predicting daytime sleepiness using wearable sensing in everyday life to raise awareness and help people to manage their energy better. This study presents a first exploration of comparing body temperature (wrist, forehead, in-ear) with users alertness, measured over a reaction test: Psychomotor vigilance task (PVT) in 7 participants over 2 days in real-life conditions (168 hours in total). The results indicate a weak correlation between some body temperature measures and the PVT scores for certain subjects. This underlines that unobtrusive on-body temperature sensing can be an interesting modality to understand and explore daytime sleepiness.
{"title":"Sleepy watch: towards predicting daytime sleepiness based on body temperature","authors":"Jie Bao, Jiawen Han, Akira Kato, K. Kunze","doi":"10.1145/3410530.3414415","DOIUrl":"https://doi.org/10.1145/3410530.3414415","url":null,"abstract":"Daytime sleepiness, the difficulty to maintain an alert waking state during the day, is a serious problem causing vehicle accidents and adverse effects on well-being, health, and productivity. Our research aims at predicting daytime sleepiness using wearable sensing in everyday life to raise awareness and help people to manage their energy better. This study presents a first exploration of comparing body temperature (wrist, forehead, in-ear) with users alertness, measured over a reaction test: Psychomotor vigilance task (PVT) in 7 participants over 2 days in real-life conditions (168 hours in total). The results indicate a weak correlation between some body temperature measures and the PVT scores for certain subjects. This underlines that unobtrusive on-body temperature sensing can be an interesting modality to understand and explore daytime sleepiness.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"51 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91464539","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 the real-world ubiquitous computing systems, it is difficult to require a significant amount of data to obtain accurate information through pure data-driven methods. The performance of data-driven methods relies on the quantity and 'quality' of data. They perform well when a sufficient amount of data is available, which is regarded as ideal conditions. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. On the other hand, it is promising to utilize physical knowledge to alleviate these issues of data limitation. The physical knowledge includes domain knowledge from experts, heuristics from experiences, analytic models of the physical phenomena and etc. The goal of the workshop is to explore the intersection between (and the combination of) data and physical knowledge. The workshop aims to bring together domain experts that explore the physical understanding of the data, practitioners that develop systems and the researchers in traditional data-driven domains. The workshop welcomes papers, which focuses on addressing these issues in different applications/domains as well as algorithmic and systematic approaches to applying physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from physical knowledge. Preliminary and on-going work is welcomed.
{"title":"CPD 2020: the 3rd international workshop on combining physical and data-driven knowledge in ubiquitous computing","authors":"Xinlei Chen, Shijia Pan, M. Amini","doi":"10.1145/3410530.3414617","DOIUrl":"https://doi.org/10.1145/3410530.3414617","url":null,"abstract":"In the real-world ubiquitous computing systems, it is difficult to require a significant amount of data to obtain accurate information through pure data-driven methods. The performance of data-driven methods relies on the quantity and 'quality' of data. They perform well when a sufficient amount of data is available, which is regarded as ideal conditions. However, in real-world systems, collecting data can be costly or impossible due to practical limitations. On the other hand, it is promising to utilize physical knowledge to alleviate these issues of data limitation. The physical knowledge includes domain knowledge from experts, heuristics from experiences, analytic models of the physical phenomena and etc. The goal of the workshop is to explore the intersection between (and the combination of) data and physical knowledge. The workshop aims to bring together domain experts that explore the physical understanding of the data, practitioners that develop systems and the researchers in traditional data-driven domains. The workshop welcomes papers, which focuses on addressing these issues in different applications/domains as well as algorithmic and systematic approaches to applying physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from physical knowledge. Preliminary and on-going work is welcomed.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"138 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91466700","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}
One component of smart or major cities are large public displays that are used for many purposes - advertising, warning, informing. These devices appeal to a large heterogeneous user group as especially in major cities people from many countries and cultures live together. Even though, researchers have identified intercultural usability recommendations for general and private devices - mobile phones, websites, etc. -, there is still a gap of information about how to design large non-personal, public displays for intercultural user groups. This dissertation explores the challenges and opportunities within the intercultural design space of large public displays. The expected results are design recommendations and guidelines regarding relevant aspects for intercultural usability of large public displays. Further, these results can be used by user interface designers and researchers that want to design, improve, evaluate or further explore large public displays for intercultural settings.
{"title":"Intercultural usability of large public displays","authors":"Laura Stojko","doi":"10.1145/3410530.3414330","DOIUrl":"https://doi.org/10.1145/3410530.3414330","url":null,"abstract":"One component of smart or major cities are large public displays that are used for many purposes - advertising, warning, informing. These devices appeal to a large heterogeneous user group as especially in major cities people from many countries and cultures live together. Even though, researchers have identified intercultural usability recommendations for general and private devices - mobile phones, websites, etc. -, there is still a gap of information about how to design large non-personal, public displays for intercultural user groups. This dissertation explores the challenges and opportunities within the intercultural design space of large public displays. The expected results are design recommendations and guidelines regarding relevant aspects for intercultural usability of large public displays. Further, these results can be used by user interface designers and researchers that want to design, improve, evaluate or further explore large public displays for intercultural settings.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86430294","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}
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
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