University of Massachusetts Medical School Faculty Publications


A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

UMMS Affiliation

Department of Medicine

Publication Date


Document Type



Analytical, Diagnostic and Therapeutic Techniques and Equipment | Artificial Intelligence and Robotics | Dietetics and Clinical Nutrition | Health Services Administration | Health Services Research | Telemedicine | Theory and Algorithms


Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.


Mobile applications, object recognition, deep learning, food recognition

DOI of Published Version



C. Liu et al., "A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure," in IEEE Transactions on Services Computing, vol. 11, no. 2, pp. 249-261, 1 March-April 2018, doi: 10.1109/TSC.2017.2662008.

Journal/Book/Conference Title

IEEE Transactions on Services Computing