Recognition of nutrition intake using time-frequency decomposition in a wearable necklace using a piezoelectric sensor

Nabil Alshurafa, Haik Kalantarian, Mohammad Pourhomayoun, Jason J. Liu, Shruti Sarin, Behnam Shahbazi, Majid Sarrafzadeh

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


Food intake levels, hydration, ingestion rate, and dietary choices are all factors known to impact the risk of obesity. This paper presents a novel wearable system in the form of a necklace, which aggregates data from an embedded piezoelectric sensor capable of detecting skin motion in the lower trachea during ingestion. The skin motion produces an output voltage with varying frequencies over time. As a result, we propose an algorithm based on time-frequency decomposition, spectrogram analysis of piezoelectric sensor signals, to accurately distinguish between food types, such as liquid and solid, hot and cold drinks, and hard and soft foods. The necklace transmits data to a smartphone, which performs the processing of the signals, classifies the food type, and provides visual feedback to the user to assist the user in monitoring their eating habits over time. We compare our spectrogram analysis with other time-frequency features, such as matching pursuit and wavelets. Experimental results demonstrate promise in using time-frequency features, with high accuracy of distinguishing between food categories using spectrogram analysis and extracting key features representative of the unique swallow patterns of various foods.

Original languageEnglish (US)
Article number7039196
Pages (from-to)3909-3916
Number of pages8
JournalIEEE Sensors Journal
Issue number7
StatePublished - Jul 1 2015


  • Classification
  • Machine learning
  • Nutrition monitoring
  • Piezoelectric sensor
  • Spectrogram analysis
  • Wearable necklace

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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