WiEat：利用Wi-Fi信号的细粒度无设备饮食监控
WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals
摘要：
吃是人们日常生活中的一项基本活动。研究表明，许多与健康有关的问题，如肥胖、糖尿病和贫血，都与人们不健康的饮食习惯密切相关（例如，不吃饭、饮食不规律和暴饮暴食）。传统的饮食监测解决方案依赖于自我报告仍然是一项繁重的任务，而最近的趋势要求用户佩戴昂贵的专用硬件仍然是侵入性的。为了克服这些限制，在本文中，我们开发了一种使用WiFi设备（例如，智能手机或膝上型电脑）。我们的系统旨在通过识别细粒度的进食动作和检测咀嚼和吞咽来自动监测用户的进食活动。特别地，我们的系统从WiFi信号中提取细粒度的信道状态信息（CSI），以区分进食与非进食活动，并进一步识别用户使用不同餐具（例如，使用民俗，刀，勺子或赤手空拳）。此外，该系统具有识别咀嚼和吞咽的能力，通过检测用户的面部肌肉运动的基础上推导出的CSI频谱。这种细粒度的进食监测结果有利于理解用户的进食行为，并且可以用于估计食物摄入类型和量。20个用户超过1600分钟的饮食的广泛实验表明，该系统可以识别用户的饮食运动高达95%的准确率和估计咀嚼和吞咽量与10%的百分比误差。
英文摘要：
Eating is a fundamental activity in people's daily life. Studies have shown that many health-related problems such as obesity, diabetes and anemia are closely associated with people's unhealthy eating habits (e.g., skipping meals, eating irregularly and overeating). Traditional eating monitoring solutions relying on self-reports remain an onerous task, while the recent trend requiring users to wear expensive dedicated hardware is still invasive. To overcome these limitations, in this paper, we develop a device-free eating monitoring system using WiFi-enabled devices (e.g., smartphone or laptop). Our system aims to automatically monitor users' eating activities through identifying the fine-grained eating motions and detecting the chewing and swallowing. In particular, our system extracts the fine-grained Channel State Information (CSI) from WiFi signals to distinguish eating from non-eating activities and further recognizing users' detailed eating motions with different utensils (e.g., using a folk, knife, spoon or bare hands). Moreover, the system has the capability of identifying chewing and swallowing through detecting users' minute facial muscle movements based on the derived CSI spectrogram. Such fine-grained eating monitoring results are beneficial to the understanding of the user's eating behaviors and can be used to estimate food intake types and amounts. Extensive experiments with 20 users over 1600-minute eating show that the proposed system can recognize the user's eating motions with up to 95% accuracy and estimate the chewing and swallowing amount with 10% percentage error.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
