This dissertation presents a framework of wearable food and drink intake monitoring system that analyzes human breathing signal for identifying swallows during the intake process. The system works based on a key observation that a person's otherwise continuous breathing cycles are interrupted by brief intra-cycle apneas during the swallows. This dissertation develops wireless wearable electronics for capturing and processing human breathing signal, and algorithms for identifying intake-related swallows via recognizing apneas extracted from breathing signal. A family of apnea detection mechanisms including matched filters and machine learning has been developed. Algorithms are developed for detecting various types of swallowing events including for solid and liquid in the presence of many artifacts presents in free-living conditions. It is demonstrated that using these algorithms and the electronics, run-time intake monitoring and analysis are feasible at acceptable accuracy levels. Further accuracy improvements were explored using a Hidden Markov Model (HMM) based mechanism that leverages known temporal locality in the human swallow sequence. Finally, it was demonstrated that by combining swallowing signatures from breathing signal with hand movement signatures using accelerometers, it is possible to train a hierarchical Support Vector Machine (SVM) classifiers and a Hidden Markov Model (HMM) for accurate mealtime and duration estimation. The developed wearable system, along with a smartphone App, was experimentally validated on tens of subjects with approval from MSU's Institutional Review Board (IRB).
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