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Early Sound-Based Meal Onset Detection Using Hidden Markov Models (HMMs)
Laura Lema-Perez1, Maja Grauff Erntsen1, Oyvind Stavdahl1
1Faculty of Information Technology and Electrical EngineeringDepartment of Engineering CyberneticsNorwegian University of Science and Technology (NTNU) Trondheim 7491 Norway.
Objective:
Meal detection is essential for automated insulin delivery systems in patients with Type 1 Diabetes (T1D), as meals are the primary drivers of rapid postprandial glucose excursions. Current closed-loop systems depend on manual meal announcements, which are often inaccurate or omitted, reducing safety and autonomy. To address this, we developed an automatic meal detector leveraging physiological sound sequences from the body.
Methods And Procedures:
The dataset comprised 50 meal recordings from ten participants (aged 24-30 years) using a throat-mounted microphone. Twenty recordings were collected in quiet conditions, while 30 included deliberate, realistic disturbances. Audio signals were preprocessed, segmented, and transformed into 46 features. Linear Discriminant Analysis (LDA) was implemented as a benchmark, while continuous Hidden Markov Model (HMM)s were applied to capture temporal dependencies and estimate meal onset probability.
Results:
Performance was evaluated using both an 80:20 train-test split and Leave-One-Subject-Out Cross-Validation (LOSO CV). Explainable Artificial Intelligence (XAI) techniques were employed to interpret model behavior and identify informative features of meal events. LDA achieved 90% accuracy, 78% sensitivity, and a detection delay of 12.3 s, while HMM reached 91% for both accuracy and sensitivity, with an average detection time of 11.7 s. Both models performed well under quiet conditions but were sensitive to background disturbances.
Conclusion:
These findings demonstrate the feasibility of detecting meal onset from physiological sounds. XAI analysis revealed that HMM effectively captures sequential transitions associated with chewing and swallowing, offering interpretable insights beyond non-temporal methods. Thus, HMM shows potential as a rapid, context-aware meal classifier for closed-loop insulin therapy, thanks to its effective modeling of temporal structure.
Clinical Impact:
This pilot study uses an innovative throat-mounted microphone to detect early meal intake by capturing chewing and swallowing sounds, offering an alternative to traditional measures such as continuous glucose monitoring (CGM).
