NIRSCam: A Mobile Near-Infrared Sensing System for Food Calorie Estimation
Haiyan Hu, Qian Zhang, Yanjiao Chen
Abstract
The calculation of calorie consumption is of paramount importance for the human diet and health management. Most existing solutions use image-processing techniques to identify the food type and refer to the nutrition table to compute the total calorie, which is quite challenging to differentiate foods that look the same but contain vastly different quantities of calories. To address this issue, we propose to leverage near-infrared spectroscopy (NIRS) to derive the concentration of nutrients based on the unique absorption spectrum of foods. Instead of using the professional NIRS system that is bulky, expensive, and impractical for daily use by nonexpert users, we develop a low-cost portable NIRS system using commercial LEDs. As the quality of the signals of the low-power LEDs is relatively poor, we carefully design modulation schemes and interference elimination algorithms to improve the signal quality and remove the interference. Extensive experiments show that NIRSCAM outperforms the image-based baseline in achieving more accurate calorie estimation, especially for look-alike foods and is robust to various environmental factors.