Hossain Delwar, Imtiaz Masudul Haider, Ghosh Tonmoy, Bhaskar Viprav, Sazonov Edward
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4191-4195. doi: 10.1109/EMBC44109.2020.9175497.
With technological advancement, wearable egocentric camera systems have extensively been studied to develop food intake monitoring devices for the assessment of eating behavior. This paper provides a detailed description of the implementation of CNN based image classifier in the Cortex-M7 microcontroller. The proposed network classifies the captured images by the wearable egocentric camera as food and no food images in real-time. This real-time food image detection can potentially lead the monitoring devices to consume less power, less storage, and more user-friendly in terms of privacy by saving only images that are detected as food images. A derivative of pre-trained MobileNet is trained to detect food images from camera captured images. The proposed network needs 761.99KB of flash and 501.76KB of RAM to implement which is built for an optimal trade-off between accuracy, computational cost, and memory footprint considering implementation on a Cortex-M7 microcontroller. The image classifier achieved an average precision of 82%±3% and an average F-score of 74%±2% while testing on 15343 (2127 food images and 13216 no food images) images of five full days collected from five participants.
随着技术的进步，可穿戴自我中心相机系统已被广泛研究，以开发用于评估饮食行为的食物摄入量监测设备。本文详细描述了基于卷积神经网络（CNN）的图像分类器在Cortex-M7微控制器中的实现。所提出的网络将可穿戴自我中心相机捕获的图像实时分类为食物图像和非食物图像。这种实时食物图像检测可能会使监测设备消耗更少的电力、占用更少的存储空间，并且在隐私方面更用户友好，因为只保存被检测为食物图像的图像。对预训练的MobileNet的一个衍生版本进行训练，以从相机捕获的图像中检测食物图像。考虑到在Cortex-M7微控制器上的实现，所提出的网络需要761.99KB的闪存和501.76KB的随机存取存储器来实现，其构建是为了在准确性、计算成本和内存占用之间进行优化权衡。在对从五名参与者收集的五个完整日的15343张（2127张食物图像和13216张非食物图像）图像进行测试时，该图像分类器的平均精度为82%±3%，平均F值为74%±2%。
