Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, B. Reimer
Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.
{"title":"CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild","authors":"Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, B. Reimer","doi":"10.1145/3603622","DOIUrl":"https://doi.org/10.1145/3603622","url":null,"abstract":"Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45437283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01Epub Date: 2023-06-10DOI: 10.1145/3582431
Angela Mastrianni, Aleksandra Sarcevic, Allison Hu, Lynn Almengor, Peyton Tempel, Sarah Gao, Randall S Burd
Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.
{"title":"Transitioning Cognitive Aids into Decision Support Platforms: Requirements and Design Guidelines.","authors":"Angela Mastrianni, Aleksandra Sarcevic, Allison Hu, Lynn Almengor, Peyton Tempel, Sarah Gao, Randall S Burd","doi":"10.1145/3582431","DOIUrl":"10.1145/3582431","url":null,"abstract":"<p><p>Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.</p>","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10489246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10241395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to the Special Issue on Human-Centred AI in Healthcare: Challenges Appearing in the Wild","authors":"T. Andersen, F. Nunes, Lauren Wilcox, Enrico W. Coiera, Yvonne Rogers","doi":"10.1145/3589961","DOIUrl":"https://doi.org/10.1145/3589961","url":null,"abstract":"CCS Concepts: • Human-centered computing → HCI theory, concepts and models","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49422023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Bødker, Sarah E. Fox, Nicolas Lalone, Megh Marathe, R. Soden
This special issue builds on and expands HCI’s engagement with historical approaches, questioning our field’s ontological orientations and offering new methods for examining the past. The set of articles featured reinvigorates questions on whose technological labor matters, how we might challenge the racist and misogynistic consequences of HCI’s inter-disciplinary inheritances, and offer new modes of liberatory world-making. Taken together, this collection serves as an act of reclamation of the lineage of our field, providing resources and guidance for a more just present and future.
{"title":"(Re)Connecting History to the Theory and Praxis of HCI","authors":"S. Bødker, Sarah E. Fox, Nicolas Lalone, Megh Marathe, R. Soden","doi":"10.1145/3589804","DOIUrl":"https://doi.org/10.1145/3589804","url":null,"abstract":"This special issue builds on and expands HCI’s engagement with historical approaches, questioning our field’s ontological orientations and offering new methods for examining the past. The set of articles featured reinvigorates questions on whose technological labor matters, how we might challenge the racist and misogynistic consequences of HCI’s inter-disciplinary inheritances, and offer new modes of liberatory world-making. Taken together, this collection serves as an act of reclamation of the lineage of our field, providing resources and guidance for a more just present and future.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48902273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pratik Ghosh, K. Posner, Stephanie L. Hyland, William C. Van Cleve, M. Bristow, D. Long, Konstantina Palla, B. Nair, C. Fong, Ronald Pauldine, M. Vavilala, Kenton O’Hara
Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.
{"title":"Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-Technical Perspective","authors":"Pratik Ghosh, K. Posner, Stephanie L. Hyland, William C. Van Cleve, M. Bristow, D. Long, Konstantina Palla, B. Nair, C. Fong, Ronald Pauldine, M. Vavilala, Kenton O’Hara","doi":"10.1145/3589953","DOIUrl":"https://doi.org/10.1145/3589953","url":null,"abstract":"Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45068728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Mayer, Yixin Zou, Byron M. Lowens, Hunter A. Dyer, Khue Le, F. Schaub, Adam J. Aviv
Data breaches are prevalent. We provide novel insights into individuals’ awareness, perception, and responses to breaches that affect them through two online surveys: a main survey (n = 413) in which we presented participants with up to three breaches that affected them, and a follow-up survey (n = 108) in which we investigated whether the main study participants followed through with their intentions to act. Overall, 73% of participants were affected by at least one breach, but participants were unaware of 74% of breaches affecting them. While some reported intention to take action, most participants believed the breach would not impact them. We also found a sizeable intention-behavior gap. Participants did not follow through with their intention when they were apathetic about breaches, considered potential costs, forgot, or felt resigned about taking action. Our findings suggest that breached organizations should be held accountable for more proactively informing and protecting affected consumers.
{"title":"Awareness, Intention, (In)Action: Individuals’ Reactions to Data Breaches","authors":"P. Mayer, Yixin Zou, Byron M. Lowens, Hunter A. Dyer, Khue Le, F. Schaub, Adam J. Aviv","doi":"10.1145/3589958","DOIUrl":"https://doi.org/10.1145/3589958","url":null,"abstract":"Data breaches are prevalent. We provide novel insights into individuals’ awareness, perception, and responses to breaches that affect them through two online surveys: a main survey (n = 413) in which we presented participants with up to three breaches that affected them, and a follow-up survey (n = 108) in which we investigated whether the main study participants followed through with their intentions to act. Overall, 73% of participants were affected by at least one breach, but participants were unaware of 74% of breaches affecting them. While some reported intention to take action, most participants believed the breach would not impact them. We also found a sizeable intention-behavior gap. Participants did not follow through with their intention when they were apathetic about breaches, considered potential costs, forgot, or felt resigned about taking action. Our findings suggest that breached organizations should be held accountable for more proactively informing and protecting affected consumers.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45543382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fujiko Robledo Yamamoto, Janghee Cho, A. Voida, Stephen Voida
Graduate students are facing a mental health crisis due to a combination of individual, community, and societal factors. Many existing stress management interventions engage with one factor at a time, typically focusing on providing a user with data about their stress state. We conducted co-design workshops with graduate students who work closely together to explore their strategies for managing stress and to learn about what types of technologies they envision to help address their stress. Using Ecological Systems Theory as an conceptual framework, our analysis of the designs and discussions from these workshops contributes an expanded design space for stress management—one that foregrounds the affordances and challenges of designing interventions that cut across ecological systems levels along with designs that approach stress management using a broader diversity of strategies: controlling, disconnecting, and normalizing stress. We argue that this expanded design space embraces a more holistic and human approach to designing stress management technologies.
{"title":"”We are researchers, but we are also humans”: Creating a design space for managing graduate student stress","authors":"Fujiko Robledo Yamamoto, Janghee Cho, A. Voida, Stephen Voida","doi":"10.1145/3589956","DOIUrl":"https://doi.org/10.1145/3589956","url":null,"abstract":"Graduate students are facing a mental health crisis due to a combination of individual, community, and societal factors. Many existing stress management interventions engage with one factor at a time, typically focusing on providing a user with data about their stress state. We conducted co-design workshops with graduate students who work closely together to explore their strategies for managing stress and to learn about what types of technologies they envision to help address their stress. Using Ecological Systems Theory as an conceptual framework, our analysis of the designs and discussions from these workshops contributes an expanded design space for stress management—one that foregrounds the affordances and challenges of designing interventions that cut across ecological systems levels along with designs that approach stress management using a broader diversity of strategies: controlling, disconnecting, and normalizing stress. We argue that this expanded design space embraces a more holistic and human approach to designing stress management technologies.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64073360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Wisiecka, Yuumi Konishi, Krzysztof Krejtz, Mahshid Zolfaghari, B. Kopainsky, I. Krejtz, H. Koike, M. Fjeld
This paper examines the attentional mechanism of in-person collaboration by means of System Dynamics-based simulations using an eye tracking experiment. Three experimental conditions were tested: in-person collaboration, remote collaboration, and single user. We hypothesized that collaboration focuses users’ attention on key information facilitating decision-making. Collaborating participants dwelt longer on key elements of the simulation than single users. Moreover, in-person collaboration and single users yielded a strategy of decision-making similar to an optimal strategy. Finally, in-person collaboration was less cognitively demanding and of higher quality. The contribution of this paper is a deeper understanding of how in-person collaboration on a large display can help users focus their visual attention on the most important areas. With this novel understanding, we believe collaborative systems designers will be better equipped to design more effective attention-guiding mechanisms in remote collaboration systems. The present work has the potential to advance the study of collaborative, interactive technologies.
{"title":"Supporting Complex Decision-Making. Evidence from an Eye Tracking Study on In-Person and Remote Collaboration","authors":"K. Wisiecka, Yuumi Konishi, Krzysztof Krejtz, Mahshid Zolfaghari, B. Kopainsky, I. Krejtz, H. Koike, M. Fjeld","doi":"10.1145/3581787","DOIUrl":"https://doi.org/10.1145/3581787","url":null,"abstract":"This paper examines the attentional mechanism of in-person collaboration by means of System Dynamics-based simulations using an eye tracking experiment. Three experimental conditions were tested: in-person collaboration, remote collaboration, and single user. We hypothesized that collaboration focuses users’ attention on key information facilitating decision-making. Collaborating participants dwelt longer on key elements of the simulation than single users. Moreover, in-person collaboration and single users yielded a strategy of decision-making similar to an optimal strategy. Finally, in-person collaboration was less cognitively demanding and of higher quality. The contribution of this paper is a deeper understanding of how in-person collaboration on a large display can help users focus their visual attention on the most important areas. With this novel understanding, we believe collaborative systems designers will be better equipped to design more effective attention-guiding mechanisms in remote collaboration systems. The present work has the potential to advance the study of collaborative, interactive technologies.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48593179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Gao, K. T. W. Choo, Junming Cao, R. Lee, Simon T. Perrault
While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA) – a process that involves multiple researchers working together to interpret data – remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we designed and implemented CoAIcoder, a tool leveraging AI to enhance human-to-human collaboration within CQA through four distinct collaboration methods. With a between-subject design, we evaluated CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases under each collaboration method. Our findings suggest that while using a shared AI model as a mediator among coders could improve CQA efficiency and foster agreement more quickly in the early coding stage, it might affect the final code diversity. We also emphasize the need to consider the independence level when using AI to assist human-to-human collaboration in various CQA scenarios. Lastly, we suggest design implications for future AI-assisted CQA systems.
{"title":"CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis","authors":"Jie Gao, K. T. W. Choo, Junming Cao, R. Lee, Simon T. Perrault","doi":"10.1145/3617362","DOIUrl":"https://doi.org/10.1145/3617362","url":null,"abstract":"While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA) – a process that involves multiple researchers working together to interpret data – remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we designed and implemented CoAIcoder, a tool leveraging AI to enhance human-to-human collaboration within CQA through four distinct collaboration methods. With a between-subject design, we evaluated CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases under each collaboration method. Our findings suggest that while using a shared AI model as a mediator among coders could improve CQA efficiency and foster agreement more quickly in the early coding stage, it might affect the final code diversity. We also emphasize the need to consider the independence level when using AI to assist human-to-human collaboration in various CQA scenarios. Lastly, we suggest design implications for future AI-assisted CQA systems.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47886260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tianyu Ren, Dengfeng Yao, Chaoran Yang, Xinchen Kang
Chinese Sign Language (CSL) and Chinese are languages used in the Chinese mainland. As a dominant language, Chinese has great influence on all levels of CSL. CSL, as a visual sign language, is fundamentally different from Chinese in linguistic structure. Unlike English, Chinese, as a pictograph, has influence on Chinese and CSL. This study explains in detail the influence of Chinese characters on CSL at the lexical level, including many elements from Chinese, such as "仿字fangzi" (form imitating Chinese characters), "书空shukong" (writing in the air with the index finger), loan translation, finger spelling, and mouthing patterns. This influence is not a simple borrowing of Chinese characters, but a creative imitation and adaptation according to the needs of sign language to express meaning. After a long period of evolution, the characteristics of Chinese characters are naturally integrated into CSL loanwords, which makes the relationship between sign language and Chinese characters closer. CSL borrows a large number of Chinese words, most of which are signs to express non-core concepts. These borrowed signs are indispensable part of CSL sign language family, enriches sign language vocabulary, improves the accuracy of sign language expression, and plays a positive role in promoting the learning, work, and lives of deaf people.
{"title":"The Influence of Chinese Characters on Chinese Sign Language","authors":"Tianyu Ren, Dengfeng Yao, Chaoran Yang, Xinchen Kang","doi":"10.1145/3591465","DOIUrl":"https://doi.org/10.1145/3591465","url":null,"abstract":"Chinese Sign Language (CSL) and Chinese are languages used in the Chinese mainland. As a dominant language, Chinese has great influence on all levels of CSL. CSL, as a visual sign language, is fundamentally different from Chinese in linguistic structure. Unlike English, Chinese, as a pictograph, has influence on Chinese and CSL. This study explains in detail the influence of Chinese characters on CSL at the lexical level, including many elements from Chinese, such as \"仿字fangzi\" (form imitating Chinese characters), \"书空shukong\" (writing in the air with the index finger), loan translation, finger spelling, and mouthing patterns. This influence is not a simple borrowing of Chinese characters, but a creative imitation and adaptation according to the needs of sign language to express meaning. After a long period of evolution, the characteristics of Chinese characters are naturally integrated into CSL loanwords, which makes the relationship between sign language and Chinese characters closer. CSL borrows a large number of Chinese words, most of which are signs to express non-core concepts. These borrowed signs are indispensable part of CSL sign language family, enriches sign language vocabulary, improves the accuracy of sign language expression, and plays a positive role in promoting the learning, work, and lives of deaf people.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41588021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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