Source Separation for Target Enhancement of Food Intake Acoustics from Noisy Recordings

International audienceAutomatic food intake monitoring can be significantly beneficial in the fight against obesity and weight management in our society today. Different sensing modalities have been used in several research efforts to accomplish automatic food intake monitoring with acoustic sensors being the most common. In this study, we explore the ability to learn spectral patterns of food intake acoustics from a clean signal and use this learned patterns for extracting the signal of interest from a noisy recording. Using standard metrics for evaluation of blind source separation, namely signal to distortion ratio and signal to interference ratio, we observed up to 20dB improvement of separation quality in very low signal to noise ratio conditions. For more practical performance evaluation of food intake monitoring, we compared the detection accuracy for chew events on the mixed/noisy signal versus on the estimated/separated target signal. We observed up to 60% improvement in chew event detection accuracy for low signal to noise ratio conditions when using the estimated target signal compared to when using the mixed/noisy signal. – Index Terms—food intake monitoring, audio source separation , nonnegative matrix factorization, harmonizable processe

Towards automatic food intake monitoring using wearable sensor-based systems

Automatic food intake monitoring using wearable sensor-based systems is an alternative to manual self-report methods. Automatic methods aim to quantitatively track aspects related to eating, drinking and/or any form of energy consumption in an effort to encourage healthier dietary behaviors.In this dissertation, a detailed evaluation of research work in the field was undertaken to outline pros and cons of various sensing modalities for on-body use. The most relevant signal processing and machine learning techniques were identified, including best features for acoustic-, image-, and motion- based methods. To address some of the observed research gaps, we focused more on acoustic-based sensing of food intake activities and developed the first real-time swallowing detection algorithm. Following this, we introduced a tracheal activity recognition algorithm based on sub-optimally sampled acoustic signals for energy efficiency purposes. Another observed research gap relates to detecting dietary activities in noisy environments particularly for acoustic-based monitoring systems that are highly affected by background noise. To this effect, we developed a source separation method using semi-supervised non-negative matrix factorization for the enhancement of food intake acoustics in noisy recordings. We also introduced a low-cost template-matching method to detect food intake acoustics in very low signal-to-noise ratio recordings. This research work contributes to the development of a robust, sensor-based, wearable dietary monitoring system. Such a system aims to curtail the growing crisis of obesity, diabetes, eating disorders and other related chronic conditions.Ph.D