Prioleau Temiloluwa, Moore Ii Elliot, Ghovanloo Maysam
IEEE Trans Biomed Eng. 2017 Sep;64(9):2075-2089. doi: 10.1109/TBME.2016.2631246. Epub 2017 Jan 16.
The threat of obesity, diabetes, anorexia, and bulimia in our society today has motivated extensive research on dietary monitoring. Standard self-report methods such as 24-h recall and food frequency questionnaires are expensive, burdensome, and unreliable to handle the growing health crisis. Long-term activity monitoring in daily living is a promising approach to provide individuals with quantitative feedback that can encourage healthier habits. Although several studies have attempted automating dietary monitoring using wearable, handheld, smart-object, and environmental systems, it remains an open research problem. This paper aims to provide a comprehensive review of wearable and hand-held approaches from 2004 to 2016. Emphasis is placed on sensor types used, signal analysis and machine learning methods, as well as a benchmark of state-of-the art work in this field. Key issues, challenges, and gaps are highlighted to motivate future work toward development of effective, reliable, and robust dietary monitoring systems.
当今社会中肥胖、糖尿病、厌食症和贪食症的威胁促使人们对饮食监测展开了广泛研究。诸如24小时回忆法和食物频率问卷等标准的自我报告方法成本高昂、操作繁琐，且在应对日益严重的健康危机时并不可靠。日常生活中的长期活动监测是一种很有前景的方法，可为个人提供定量反馈，从而鼓励养成更健康的习惯。尽管已有多项研究尝试利用可穿戴设备、手持设备、智能物体和环境系统实现饮食监测自动化，但这仍是一个开放的研究问题。本文旨在对2004年至2016年期间的可穿戴和手持方法进行全面综述。重点介绍了所使用的传感器类型、信号分析和机器学习方法，以及该领域的前沿工作基准。文中突出了关键问题、挑战和差距，以推动未来朝着开发有效、可靠且强大的饮食监测系统的方向开展工作。
