School of Technology, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, Portugal.
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal.
Sensors (Basel). 2022 Aug 26;22(17):6443. doi: 10.3390/s22176443.
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer's disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.
如今，人们的生活压力很大，这影响了他们的饮食习惯。在生命早期，青少年开始表现出不良习惯和营养不足。同样，其他患有痴呆症、阿尔茨海默病或其他疾病的人可能无法定期进食或服药。因此，监测能力对他们和可以分析饮食习惯模式及其与整体健康相关性的医生都可能是有益的。许多传感器有助于准确检测食物摄入情况，包括胃电图、摄像头、麦克风和惯性传感器。准确的检测可以提供更好的控制，从而养成健康的营养习惯。本文对用于食物摄入检测的技术进行了系统综述，重点介绍了使用的不同传感器和方法。该搜索是使用自然语言处理 (NLP) 框架进行的，该框架有助于筛选不相关的研究，同时遵循 PRISMA 方法。它自动在不同的数据库中搜索和筛选研究论文，包括 PubMed、Springer、ACM、IEEE Xplore、MDPI 和 Elsevier。然后，根据框架的结果进行手动分析，选择了 30 篇论文进行进一步分析，这表明人们对使用传感器进行食物摄入检测和营养评估的兴趣浓厚。主要使用的传感器是摄像头、惯性和声学传感器，它们使用人工智能技术来识别食物摄入情况。这项研究确定了最常用的传感器和数据处理方法来检测食物摄入。
