论文标题
咀嚼商业智能玻璃的检测
Chewing Detection from Commercial Smart-glasses
论文作者
论文摘要
在过去的几年中，自动饮食监测已取得了显着发展，在传感器和算法方面以及对饮食行为的哪些方面或参数进行了多种解决方案。经过广泛研究了基于咀嚼声的自动检测，但是，它需要将麦克风安装在受试者的头上以捕获相关声音。在这项工作中，我们评估了使用现成的商业设备，即Razer Anzu Smart-Glasses进行自动咀嚼检测的可行性。智能玻璃配备了立体声扬声器和麦克风，可通过蓝牙与智能手机通信。麦克风放置并不是捕获咀嚼声音的最佳选择，但是，我们发现它不会显着影响检测有效性。我们应用了文献中的算法，并对我们在内部收集的具有挑战性的数据集进行了一些调整。一对一的受试者实验得出令人有希望的结果，最佳基于持续时间的饮食时间评估的F1得分为0.96。
Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored. Automatic detection of eating based on chewing sounds has been studied extensively, however, it requires a microphone to be mounted on the subject's head for capturing the relevant sounds. In this work, we evaluate the feasibility of using an off-the-shelf commercial device, the Razer Anzu smart-glasses, for automatic chewing detection. The smart-glasses are equipped with stereo speakers and microphones that communicate with smart-phones via Bluetooth. The microphone placement is not optimal for capturing chewing sounds, however, we find that it does not significantly affect the detection effectiveness. We apply an algorithm from literature with some adjustments on a challenging dataset that we have collected in house. Leave-one-subject-out experiments yield promising results, with an F1-score of 0.96 for the best case of duration-based evaluation of eating time.
