论文标题
你过着健康的生活吗？通过视觉生活记录分析生活方式
Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging
论文作者
论文摘要
健康的生活方式是改善健康和幸福的关键，对生活质量和疾病的预防质量有很大影响。当前的生命式/以自我为中心的数据集不适合生活方式分析；因此，在计算机视觉领域没有关于生活方式分析的研究。在这项工作中，我们研究了生活方式分析的问题，并构建了视觉寿命数据集以进行生活方式分析（VLDLA）。 VLDLA包含每3秒从8:00 AM到6:00 PM捕获的图像，持续7天。与当前的LifeLoging/EgoCentric数据集相反，我们的数据集适用于生活方式分析，因为图像是在短时间间隔拍摄的，以捕获短持续时间的活动；此外，图像是从早晨到晚上连续拍摄的，以记录用户执行的所有活动。基于我们的数据集，我们将每个帧中的用户活动分类，并使用用户的三种潜在流利，随着时间的流逝会随时间变化并与活动相关联，以衡量用户生活方式的健康程度。三种潜在流利的分数是根据公认活动计算的，并且根据潜在流利的分数确定一天的健康生活方式。实验结果表明，我们的方法可用于分析用户生活方式的健康状况。
A healthy lifestyle is the key to better health and happiness and has a considerable effect on quality of life and disease prevention. Current lifelogging/egocentric datasets are not suitable for lifestyle analysis; consequently, there is no research on lifestyle analysis in the field of computer vision. In this work, we investigate the problem of lifestyle analysis and build a visual lifelogging dataset for lifestyle analysis (VLDLA). The VLDLA contains images captured by a wearable camera every 3 seconds from 8:00 am to 6:00 pm for seven days. In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user. Based on our dataset, we classify the user activities in each frame and use three latent fluents of the user, which change over time and are associated with activities, to measure the healthy degree of the user's lifestyle. The scores for the three latent fluents are computed based on recognized activities, and the healthy degree of the lifestyle for the day is determined based on the scores for the latent fluents. Experimental results show that our method can be used to analyze the healthiness of users' lifestyles.
