{"549871":{"#nid":"549871","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Temiloluwa Olubanjo","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003ETowards Automatic Food Intake Monitoring using Wearable Sensor-based Systems\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Maysam Ghovanloo, ECE, Chair , Advisor\u003C\/p\u003E\u003Cp\u003EDr. Elliot Moore, ECE, Co-Advisor\u003C\/p\u003E\u003Cp\u003EDr. Omer Inan, ECE\u003C\/p\u003E\u003Cp\u003EDr. Gregory Abowd, IC\u003C\/p\u003E\u003Cp\u003EDr. Fatih Sarioglu, ECE\u003C\/p\u003E\u003Cp\u003EDr. Thad Starner, CoC\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAutomatic 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.\u0026nbsp;In this dissertation, a detailed evaluation of research work\u0026nbsp;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.\u0026nbsp;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.\u0026nbsp;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.\u0026nbsp;\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Towards Automatic Food Intake Monitoring using Wearable Sensor-based Systems"}],"uid":"28475","created_gmt":"2016-06-30 16:57:08","changed_gmt":"2016-10-08 02:18:15","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-07-15T12:00:00-04:00","event_time_end":"2016-07-15T12:00:00-04:00","event_time_end_last":"2016-07-15T12:00:00-04:00","gmt_time_start":"2016-07-15 16:00:00","gmt_time_end":"2016-07-15 16:00:00","gmt_time_end_last":"2016-07-15 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"1808","name":"graduate students"},{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}