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High-resolution photo-mosaic time-series imagery for monitoring human use of an artificial reef.用于监测人工鱼礁人类使用情况的高分辨率照片拼接时间序列图像。
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关于肥胖相关行为和环境的独特观点:使用静态和视频图像的研究

Unique Views on Obesity-Related Behaviors and Environments: Research Using Still and Video Images.

作者信息

Carlson Jordan A, Hipp J Aaron, Kerr Jacqueline, Horowitz Todd S, Berrigan David

机构信息

Children's Mercy Kansas City.

North Carolina State University.

出版信息

J Meas Phys Behav. 2018 Sep;1(3):143-154. doi: 10.1123/jmpb.2018-0021.

DOI:10.1123/jmpb.2018-0021
PMID:31263802
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6602079/
Abstract

OBJECTIVES

To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity- or nutrition-related behaviors and/or environments.

METHODS

Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study.

RESULTS

Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%).

CONCLUSIONS

Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.

摘要

目的

通过收集此类研究的实例来记录涉及使用图像评估身体活动或营养相关行为和/或环境的研究面临的挑战和益处。

方法

通过本文作者的知识和网络以及文献检索,确定在研究中使用图像捕捉的研究人员(即关键信息提供者)。29名关键信息提供者完成了一项专门为本研究制定的调查,内容涵盖研究类型、图像来源以及所经历的挑战和益处。

结果

大多数受访者在研究中使用静态图像,只有26.7%使用视频。图像来源分为参与者生成(n = 13;例如,参与者使用智能手机进行饮食评估)、研究人员生成(n = 10;例如,具有自动图像捕捉功能的可穿戴相机)或从第三方策划(n = 7;例如,谷歌街景)。出现的两个主要挑战包括需要对大型数据集进行自动化处理(58.8%)和参与者招募/依从性(41.2%)。与益处相关的主题包括随着数据覆盖范围增加对肥胖有了更多视角(34.6%)以及行为和环境评估的准确性提高(34.6%)。

结论

技术进步将支持在身体活动、营养行为和环境评估中更多地使用图像。为了推进这一研究领域,健康领域和计算机科学家之间需要更有效的合作。特别是需要针对行为、环境和食物特征的不同方面开发自动化数据提取方法。此外,在解决与研究目的图像捕捉相关的伦理问题的标准方面取得进展至关重要。