标题:A Multi-sensor Framework for Personal Presentation Analytics
作者:Gan, Tian; Li, Junnan; Wong, Yongkang; Kankanhalli, Mohan S.
作者机构:[Gan, Tian] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.; [Li, Junnan] Natl Univ Singapore, NUS Grad Sch Integrat Sci & 更多
通讯作者:Gan, T
通讯作者地址:[Gan, T]Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.
来源:ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版年:2019
卷:15
期:2
DOI:10.1145/3300941
关键词:Quantified self; multi-modal analysis; presentations; learning analytics
摘要:Presentation has been an effective method for delivering information to an audience for many years. Over the past few decades, technological advancements have revolutionized the way humans deliver presentation. Conventionally, the quality of a presentation is usually evaluated through painstaking manual analysis with experts. Although the expert feedback is effective in assisting users to improve their presentation skills, manual evaluation suffers from high cost and is often not available to most individuals. In this work, we propose a novel multi-sensor self-quantification system for presentations, which is designed based on a new proposed assessment rubric. We present our analytics model with conventional ambient sensors (i.e., static cameras and Kinect sensor) and the emerging wearable egocentric sensors (i.e., Google Glass). In addition, we performed a cross-correlation analysis of speaker's vocal behavior and body language. The proposed framework is evaluated on a new presentation dataset, namely, NUS Multi-Sensor Presentation dataset, which consists of 51 presentations covering a diverse range of topics. To validate the efficacy of the proposed system, we have conducted a series of user studies with the speakers and an interview with an English communication expert, which reveals positive and promising feedback.
收录类别:EI;SCOPUS;SCIE;SSCI
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067240351&doi=10.1145%2f3300941&partnerID=40&md5=9114cb417e537f959a7a30a9cb8ce5f4
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