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<p>Camera-based systems offer a comprehensive and inconspicuous approach to monitoring the well-being of individuals within the comfort of their homes. This study introduces a vision-based, fully autonomous pipeline for assessing eating behaviors and detecting musculoskeletal changes. The system captures eating activities and provides detailed insights such as hand-to-mouth motion duration and bite count. These indicators are vital for understanding behavioral and physiological influences on food consumption and their associated changes. The system integrates pose estimation and a temporal action localization network to classify actions and generate behavior profiles. Evaluated on the EatSense dataset and a supplementary test set, the system achieves strong performance, including a mean average precision (mAP) of 74% at 0.10 IoU for micro-action detection and a posture anomaly detection accuracy of over 76%. These results demonstrate the system’s ability to detect subtle trends such as slower hand movements under increased wrist weights and changes in chewing behavior. Additionally, comparisons against Gemini-2.5-Pro, a state-of-the-art multimodal model, reinforces the system’s accuracy. So, by successfully capturing trends aligned with ground truth data, the pipeline shows promise for long-term health monitoring, early detection of musculoskeletal decline, and behavioral changes in dietary habits—offering potential applications in elderly care and remote health assessment. The new test dataset is released on <a href="https://groups.inf.ed.ac.uk/vision/DATASETS/EATSENSE/">https://groups.inf.ed.ac.uk/vision/DATASETS/EATSENSE/</a>.</p>

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A Vision-based System for Monitoring Eating Behaviors and Musculoskeletal Function

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Muhammad Ahmed Raza,
  • Robert B. Fisher

摘要

Camera-based systems offer a comprehensive and inconspicuous approach to monitoring the well-being of individuals within the comfort of their homes. This study introduces a vision-based, fully autonomous pipeline for assessing eating behaviors and detecting musculoskeletal changes. The system captures eating activities and provides detailed insights such as hand-to-mouth motion duration and bite count. These indicators are vital for understanding behavioral and physiological influences on food consumption and their associated changes. The system integrates pose estimation and a temporal action localization network to classify actions and generate behavior profiles. Evaluated on the EatSense dataset and a supplementary test set, the system achieves strong performance, including a mean average precision (mAP) of 74% at 0.10 IoU for micro-action detection and a posture anomaly detection accuracy of over 76%. These results demonstrate the system’s ability to detect subtle trends such as slower hand movements under increased wrist weights and changes in chewing behavior. Additionally, comparisons against Gemini-2.5-Pro, a state-of-the-art multimodal model, reinforces the system’s accuracy. So, by successfully capturing trends aligned with ground truth data, the pipeline shows promise for long-term health monitoring, early detection of musculoskeletal decline, and behavioral changes in dietary habits—offering potential applications in elderly care and remote health assessment. The new test dataset is released on https://groups.inf.ed.ac.uk/vision/DATASETS/EATSENSE/.