Yafei Nie , Shurong Tong , Jing Li , Yicha Zhang , Chen Zheng , Bin Fan
{"title":"基于认知负荷测量的设计知识推送时间识别","authors":"Yafei Nie , Shurong Tong , Jing Li , Yicha Zhang , Chen Zheng , Bin Fan","doi":"10.1016/j.aei.2022.101783","DOIUrl":null,"url":null,"abstract":"<div><p>The reuse of multidisciplinary design knowledge is pivotal in product development because of the increasingly fierce market competition. It can assist designers, particularly those who lack sufficient experience, in making correct decisions and achieving rapid design. Traditionally, designers primarily acquire design knowledge through information retrieval, which is typically time-consuming and inefficient. A solution that is widely considered to overcome the deficiencies of traditional knowledge retrieval approaches and actively provide designers with necessary knowledge is knowledge push. However, achieving the timely push of required knowledge to designers during the design process remains a challenging task. Accordingly, this paper presents a time identification method based on cognitive load measurement to identify the suitable time for design knowledge push. First, behavioral indicators related to the changes in cognitive load are identified by investigating the influence of the load on three types of behaviors: mouse dynamics, keystroke dynamics, and emotional states. Second, the possibility and efficacy of inferring the cognitive load by simultaneously and unobtrusively tracking the three aforementioned behaviors are considered through behavioral observations. Finally, predicting the knowledge push time based on the cognitive load using classification algorithms is investigated. The experimental results indicate that the accuracy of the proposed method in inferring the cognitive load is 55%, and that of push time is 83%.</p></div>","PeriodicalId":8,"journal":{"name":"ACS Biomaterials Science & Engineering","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Time identification of design knowledge push based on cognitive load measurement\",\"authors\":\"Yafei Nie , Shurong Tong , Jing Li , Yicha Zhang , Chen Zheng , Bin Fan\",\"doi\":\"10.1016/j.aei.2022.101783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The reuse of multidisciplinary design knowledge is pivotal in product development because of the increasingly fierce market competition. It can assist designers, particularly those who lack sufficient experience, in making correct decisions and achieving rapid design. Traditionally, designers primarily acquire design knowledge through information retrieval, which is typically time-consuming and inefficient. A solution that is widely considered to overcome the deficiencies of traditional knowledge retrieval approaches and actively provide designers with necessary knowledge is knowledge push. However, achieving the timely push of required knowledge to designers during the design process remains a challenging task. Accordingly, this paper presents a time identification method based on cognitive load measurement to identify the suitable time for design knowledge push. First, behavioral indicators related to the changes in cognitive load are identified by investigating the influence of the load on three types of behaviors: mouse dynamics, keystroke dynamics, and emotional states. Second, the possibility and efficacy of inferring the cognitive load by simultaneously and unobtrusively tracking the three aforementioned behaviors are considered through behavioral observations. Finally, predicting the knowledge push time based on the cognitive load using classification algorithms is investigated. The experimental results indicate that the accuracy of the proposed method in inferring the cognitive load is 55%, and that of push time is 83%.</p></div>\",\"PeriodicalId\":8,\"journal\":{\"name\":\"ACS Biomaterials Science & Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2022-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Biomaterials Science & Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1474034622002415\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Biomaterials Science & Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034622002415","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Time identification of design knowledge push based on cognitive load measurement
The reuse of multidisciplinary design knowledge is pivotal in product development because of the increasingly fierce market competition. It can assist designers, particularly those who lack sufficient experience, in making correct decisions and achieving rapid design. Traditionally, designers primarily acquire design knowledge through information retrieval, which is typically time-consuming and inefficient. A solution that is widely considered to overcome the deficiencies of traditional knowledge retrieval approaches and actively provide designers with necessary knowledge is knowledge push. However, achieving the timely push of required knowledge to designers during the design process remains a challenging task. Accordingly, this paper presents a time identification method based on cognitive load measurement to identify the suitable time for design knowledge push. First, behavioral indicators related to the changes in cognitive load are identified by investigating the influence of the load on three types of behaviors: mouse dynamics, keystroke dynamics, and emotional states. Second, the possibility and efficacy of inferring the cognitive load by simultaneously and unobtrusively tracking the three aforementioned behaviors are considered through behavioral observations. Finally, predicting the knowledge push time based on the cognitive load using classification algorithms is investigated. The experimental results indicate that the accuracy of the proposed method in inferring the cognitive load is 55%, and that of push time is 83%.
期刊介绍:
ACS Biomaterials Science & Engineering is the leading journal in the field of biomaterials, serving as an international forum for publishing cutting-edge research and innovative ideas on a broad range of topics:
Applications and Health – implantable tissues and devices, prosthesis, health risks, toxicology
Bio-interactions and Bio-compatibility – material-biology interactions, chemical/morphological/structural communication, mechanobiology, signaling and biological responses, immuno-engineering, calcification, coatings, corrosion and degradation of biomaterials and devices, biophysical regulation of cell functions
Characterization, Synthesis, and Modification – new biomaterials, bioinspired and biomimetic approaches to biomaterials, exploiting structural hierarchy and architectural control, combinatorial strategies for biomaterials discovery, genetic biomaterials design, synthetic biology, new composite systems, bionics, polymer synthesis
Controlled Release and Delivery Systems – biomaterial-based drug and gene delivery, bio-responsive delivery of regulatory molecules, pharmaceutical engineering
Healthcare Advances – clinical translation, regulatory issues, patient safety, emerging trends
Imaging and Diagnostics – imaging agents and probes, theranostics, biosensors, monitoring
Manufacturing and Technology – 3D printing, inks, organ-on-a-chip, bioreactor/perfusion systems, microdevices, BioMEMS, optics and electronics interfaces with biomaterials, systems integration
Modeling and Informatics Tools – scaling methods to guide biomaterial design, predictive algorithms for structure-function, biomechanics, integrating bioinformatics with biomaterials discovery, metabolomics in the context of biomaterials
Tissue Engineering and Regenerative Medicine – basic and applied studies, cell therapies, scaffolds, vascularization, bioartificial organs, transplantation and functionality, cellular agriculture