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January 1, 2026The Journal of Japanese Society of Stomatognathic FunctionOpen Access

Development of a Chewing Motion Recognition Algorithm Using a Depth Camera and Markerless Motion Capture

Ayaka Murashima Yousuke Manda Jumpei Okawa Kota Amano Yasuhiko Oga Hisaki Ogihara Satoko Takeuchi Miki Nakaza Naohiro Nishimura Akitomo Watanabe Naoki Kodama

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Key Points

The aim is to develop an effective algorithm for recognizing chewing motions using advanced imaging technology.
Utilized a depth camera for capturing motion data without physical markers.
Employed machine learning techniques to analyze and classify chewing movements.
Tested the algorithm's accuracy in recognizing various chewing patterns.
The algorithm demonstrated a recognition accuracy of over 85%.
Successful classification of different chewing speeds and patterns.
Potential implications for applications in health monitoring and dietary assessment.

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Implication

Development of an algorithm assessing chewing movement using a depth camera, indicating its potential applications.

Abstract

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Cite This Study

Murashima et al. (2026) studied this question.

synapsesocial.com/papers/6a02c2fdce8c8c81e9640501 https://doi.org/10.7144/sgf.32.112

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Algorithm and Marker Development for 6D Motion Measurement Using a Single Camera2025 · 1 citations
2Design of Low-Cost High Accuracy Motion Capture SystemOriginal Research · 2026
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4Markerless Facial Reconstruction Motion Capture Using Triangulation Method2025
5Development of Marker-Based Motion Capture Using RGB Cameras: A Neural Network Approach for Spherical Marker Detection2025

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