JMIR Publications

ABSTRACT

Background:

Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and, suffer from lack of accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision has the potential to facilitate reliable quantification of food intake.

Objective:

To evaluate the accuracy of a novel smartphone application combining depth-sensing hardware with computer vision to quantify meal macronutrient content.

Methods:

The application ran on a smartphone with built-in depth sensor applying structured light (iPhone X) and estimated weight, macronutrient (carbohydrate, protein, fat) and energy content of 48 randomly chosen meals (type of meals: breakfast, cooked meals, snacks) encompassing 128 food items. Reference weight was generated by weighing individual food items using a precision scale. The study endpoints were fourfold: i) error of estimated meal weight; ii) error of estimated meal macronutrient content and energy content; iii) segmentation performance; and iv) processing time.

Results:

Mean±SD absolute error of the application’s estimate was 35.1±42.8g (14.0±12.2%) for weight, 5.5±5.1g (14.8±10.9%) for carbohydrate content, 2.4±5.6g (13.0±13.8%), 1.3±1.7g (12.3±12.8%) for fat content and 41.2±42.5kcal (12.7±10.8%) for energy content. While estimation accuracy was not affected by the viewing angle, the type of meal mattered with slightly worse performance for cooked meals compared to breakfast and snack. Segmentation required adjustment for 7 out of 128 items. Mean±SD processing time across all meals was 22.9±8.6s.

Conclusions:

The present study evaluated the accuracy of a novel smartphone application with integrated depth-sensing camera and found a high accuracy in food estimation across all macronutrients. This was paralleled by a high segmentation performance and low processing time corroborating the high usability of this system.