Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system

Abstract

Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

Development and validation of a point‐of‐care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study

“…In addition, the WeChat mini-program is compatible with various mobile operating systems, making it highly portable and accessible. 20,45 There are some limitations that need to be addressed. First, the validation of the model was only conducted on tumor patients in the second external verification set, and other types of patients were not included.…”

“…Our screening tool successfully makes the evaluation of nutritional status objective and visual, while simplifying the assessment and monitoring process for clinical doctors and nursing staff. In addition, the WeChat mini‐program is compatible with various mobile operating systems, making it highly portable and accessible 20,45 …”

“…In addition, the WeChat mini‐program is compatible with various mobile operating systems, making it highly portable and accessible. 20 , 45 …”