Food Intake Activities Using Sensors with Heterogeneous Privacy Sensitivity Levels

Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones. As such, food intake activity recognition in particular is under investigation in the literature despite its importance for human health and well-being, including telehealth and diet management. In order to determine sensors’ practical recognition accuracy, preferably with the least amount of privacy intrusion, a dataset of food intake activities utilizing sensors with varying degrees of privacy sensitivity is required. In this project, we collected such a dataset by collecting fine-grained food intake activities using sensors of heterogeneous privacy sensitivity levels, namely a mmWave radar, an RGB camera, and a depth camera. Solutions to recognize food intake activities can be developed using this dataset, which may provide a more comprehensive picture of the accuracy and privacy trade-offs involved with heterogeneous sensors.

人类活动识别，即从传感器数据中识别人类活动，随着智能家居、智慧城市和智能系统的兴起，引起了研究人员和实践者的广泛关注。现有关于活动识别的研究主要集中于粗粒度活动，如行走和跳跃，而细粒度活动，如进食和饮水，由于识别细粒度活动比粗粒度活动更为复杂，因而研究不足。因此，尽管食物摄入活动对于人类健康和福祉，包括远程医疗和饮食管理具有重要意义，但其在文献中仍处于研究阶段。为了确定传感器的实际识别准确率，并在尽可能减少隐私侵犯的情况下进行，需要一套使用不同隐私敏感度等级的传感器进行食物摄入活动的数据集。在本项目中，我们通过收集不同隐私敏感度等级的传感器（即毫米波雷达、RGB摄像头和深度摄像头）所监测的细粒度食物摄入活动，构建了此类数据集。利用该数据集可开发出识别食物摄入活动的方法，这或许能为我们提供一个关于使用异构传感器所涉及的准确率与隐私权衡的更为全面的图景。