A Wearable Nutrition Monitoring System

Kalantarian, Haik; Alshurafa, Nabil; Sarrafzadeh, Majid

doi:10.1109/bsn.2014.26

Cited by 70 publications

(44 citation statements)

References 14 publications

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“…Current systems either have low accuracy in detecting swallows and distinguishing food types, or must be uncomfortably worn around the neck, which renders continuous use impractical [8]. In this paper, we focus on a piezoelectric-based design of a necklace that is not worn tightly around the neck, but rather hangs loosely and falls more naturally right above the sternum [24], [5], [20], [21], [23].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor

et al. 2015

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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 (MP) 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.

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Section: Motivation and Backgroundmentioning

confidence: 99%

Recognition of Nutrition Intake Using Time-Frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor

et al. 2015

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“…Ongoing research work in this area ranges from the use of crowdsourcing techniques [15], wearables [8, 4], and instrumented objects [6]. Sound is a contextually-rich source of information that can be easily recorded using one of the simplest and most ubiquitous sensors; a microphone.…”

Section: Related Workmentioning

confidence: 99%

Inferring Meal Eating Activities in Real World Settings from Ambient Sounds

Thomaz

Zhang

Essa

et al. 2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

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Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.

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“…56 Another device, which is designed to fit around the neck, is described as a low-cost wearable necklace embedded with piezoelectric sensors. 57 These sensors produce a voltage at their terminals in response to mechanical stress such as the motion of the throat during a swallow. The data obtained are transmitted to a mobile device capable of estimating the volume of food consumption for analysis.…”

Section: Mobile Devicesmentioning

confidence: 99%