组成图示
示意图生成中
传感器类型
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检测对象
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检测原理
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检测灵敏度
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效应效果
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传感器的构成
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中文摘要
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英文摘要
INTRODUCTION: Bruxism remains a diagnostic challenge, with no consistently reliable clinical approaches available to document the condition with satisfactory accuracy. This study aimed to incorporate a biosensor device into a conventional bite-night guard to detect bruxism in an in vitro setting.
METHODS: A sandwich-layering process was used to integrate stress and vibration sensors into an acrylic occlusal stabilization splint. The system included a microcontroller, control unit, and data acquisition module. Occlusal force signals were processed using artificial intelligence-based algorithms. A total of 200 repeated trials were conducted to evaluate system performance. Accuracy, sensitivity, and specificity were calculated as validation metrics.
RESULTS: The biosensor prototype demonstrated reliable performance across a force range of 274-700 N. Quantitative evaluation of the neural network yielded an accuracy of 91%, sensitivity of 88%, and specificity of 90% in distinguishing occlusal force thresholds.
CONCLUSION: The findings confirm the feasibility of integrating biosensors within an intraoral appliance for bruxism detection in vitro. Future research should explore long-term durability testing in moist environments and conduct in vivo trials to validate clinical performance.