通过智能团体餐饮获取饮食数据的成本（CS Society）

• 2020 年 1 月 3 日
• 筆記

随着公众对食物摄入的认识，对饮食数据管理的需求正在增长。 结果，越来越多的智能食堂部署通过基于射频识别（RFID）或基于计算机视觉（CV）的解决方案来收集饮食数据。 由于这两种情况都涉及人工，因此人力分配对于数据质量至关重要。 在人力需求被低估的地方，数据质量会受到损害。 本文利用基于从多个智能食堂收集的真实数据的数值模拟，研究了膳食数据质量与投入人力之间的关系。 我们发现，在基于RFID和CV的系统中，饮食数据获取的长期成本都由人力决定。 我们的研究为饮食数据获取的成本构成提供了全面的了解，并对未来具有成本效益的系统提供了有用的见解。

原文题目：Cost of Dietary Data Acquisition with Smart Group Catering

原文：The need for dietary data management is growing with public awareness of food intakes. As a result, there are increasing deployments of smart canteens where dietary data is collected through either Radio Frequency Identification (RFID) or Computer Vision(CV)-based solutions. As human labor is involved in both cases, manpower allocation is critical to data quality. Where manpower requirements are underestimated, data quality is compromised. This paper has studied the relation between the quality of dietary data and the manpower invested, using numerical simulations based on real data collected from multiple smart canteens. We found that in both RFID and CV-based systems, the long-term cost of dietary data acquisition is dominated by manpower. Our study provides a comprehensive understanding of the cost composition for dietary data acquisition and useful insights toward future cost effective systems.

原文作者：Jiapeng Dong,Pengju Wang,Weiqiang Sun

原文地址：https://arxiv.org/abs/2001.00367