Hassan M A, Sazonov E
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:7145-7148. doi: 10.1109/EMBC.2019.8857078.
Current head-mounted wearable sensors for monitoring of food intake operates by fusing multiple modalities such as inertial and image sensing. The image capture may be performed periodically, capturing a large number of irrelevant images, increasing power consumption and reducing the battery life. In this manuscript, we propose an efficient approach for food image capture, that captures the images only when the head tilt angle estimated from the accelerometer data matches that during ingestion of food. The method was developed and validated using data from 15 volunteers consuming unrestricted meals in a free-living environment between 12.5 to 18.5 hours. The tilt angle of the head was computed using 3D accelerometer data. A classifier for image capture was developed using a curve fitting approach on the tilt angles of the head. The proposed method achieved a sensitivity of 0.97 and specificity of 0.47 in predicting capture of food images, thus potentially improving the battery life of the wearable device.
当前用于监测食物摄入量的头戴式可穿戴传感器通过融合多种模式（如惯性和图像传感）来运行。图像捕获可能会定期进行，从而捕获大量无关图像，增加功耗并缩短电池寿命。在本手稿中，我们提出了一种高效的食物图像捕获方法，该方法仅在根据加速度计数据估计的头部倾斜角度与进食期间的角度匹配时才捕获图像。该方法是利用15名志愿者在12.5至18.5小时的自由生活环境中无限制进食的数据开发并验证的。使用3D加速度计数据计算头部的倾斜角度。通过对头倾斜角度进行曲线拟合的方法开发了用于图像捕获的分类器。所提出的方法在预测食物图像捕获方面的灵敏度为0.97，特异性为0.47，从而有可能提高可穿戴设备的电池寿命。
