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Improving the recognition of eating gestures using intergesture sequential dependencies.

Raul I Ramos-Garcia, Eric R Muth, John N Gowdy

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    |June 12, 2014
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    Summary
    This summary is machine-generated.

    Recognizing eating gestures using wrist motion is improved by considering the sequence of actions. Hidden Markov models (HMMs) that analyze inter-gesture dependencies significantly boost accuracy in identifying mealtime activities.

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    Area of Science:

    • Human-Computer Interaction
    • Activity Recognition
    • Machine Learning

    Background:

    • Automated recognition of eating gestures is crucial for various applications.
    • Previous research focused on individual gesture recognition, overlooking sequential patterns.
    • Understanding the temporal dynamics of eating activities is an open challenge.

    Purpose of the Study:

    • To investigate the sequential dependencies between eating gestures.
    • To improve the accuracy of eating gesture recognition by incorporating sequential context.
    • To evaluate the effectiveness of different machine learning models, including Hidden Markov Models (HMMs), for this task.

    Main Methods:

    • Development of three classifiers: K-nearest neighbor (KNN), subgesture-aware HMM, and intergesture-dependent HMMs.
    • Implementation of first-order to sixth-order HMMs to model varying levels of sequential dependence.
    • Testing on a dataset comprising 25 meals with tracked wrist motion data.

    Main Results:

    • Baseline accuracies achieved were 75.8% for KNN and 84.3% for the subgesture HMM.
    • HMMs modeling intergesture sequential dependencies reached a peak accuracy of 96.5%.
    • Higher-order HMMs demonstrated improved performance compared to lower-order models.

    Conclusions:

    • Sequential dependencies between eating gestures are significant and can be effectively modeled.
    • Incorporating intergesture sequential context substantially enhances eating gesture recognition accuracy.
    • HMMs provide a powerful framework for leveraging temporal information in activity recognition.