AutoDietary: A Wearable Acoustic Sensor System for Food Intake Recognition in Daily Life

Yin Bi, Mingsong Lv, Chen Song, Wenyao Xu, Nan Guan, Wang Yi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

110 Citations (Scopus)


Nutrition-related diseases are nowadays a main threat to human health and pose great challenges to medical care. A crucial step to solve the problems is to monitor the daily food intake of a person precisely and conveniently. For this purpose, we present AutoDietary, a wearable system to monitor and recognize food intakes in daily life. An embedded hardware prototype is developed to collect food intake sensor data, which is highlighted by a high-fidelity microphone worn on the subject's neck to precisely record acoustic signals during eating in a noninvasive manner. The acoustic data are preprocessed and then sent to a smartphone via Bluetooth, where food types are recognized. In particular, we use hidden Markov models to identify chewing or swallowing events, which are then processed to extract their time/frequency-domain and nonlinear features. A lightweight decision-tree-based algorithm is adopted to recognize the type of food. We also developed an application on the smartphone, which aggregates the food intake recognition results in a user-friendly way and provides suggestions on healthier eating, such as better eating habits or nutrition balance. Experiments show that the accuracy of food-type recognition by AutoDietary is 84.9%, and those to classify liquid and solid food intakes are up to 97.6% and 99.7%, respectively. To evaluate real-life user experience, we conducted a survey, which collects rating from 53 participants on wear comfort and functionalities of AutoDietary. Results show that the current design is acceptable to most of the users.
Original languageEnglish
Article number7206521
Pages (from-to)806-816
Number of pages11
JournalIEEE Sensors Journal
Issue number3
Publication statusPublished - 1 Feb 2016
Externally publishedYes


  • Acoustic Signal Processing
  • Embedded System
  • Food Intake Recognition
  • Wearable Sensor

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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