用智能呼吸面罩识别动作，准确率超95%。
i-Mask: An Intelligent Mask for Breath-Driven Activity Recognition
- 通过内置传感器捕捉呼气模式，构建呼吸特征数据
- 在志愿者数据上实现超过95%的动作识别准确率
- 适合健康监测与运动追踪场景，无需穿戴复杂设备
吸气与呼气的模式包含重要的生理信号，可用于预测人类行为、健康趋势和生命体征。人体活动识别（HAR）与这些生命体征密切相关，能提供更深入的健康洞察并实现实时监测。本文提出i-Mask，一种新型HAR方法，利用自研呼吸面罩集成传感器捕捉呼出气体模式。志愿者佩戴该面罩收集的数据经过降噪、时间序列分解和标注后，用于训练预测模型。实验结果验证了该方法的有效性，在多个任务中达到超过95%的准确率，展现出在医疗与健身领域的应用潜力。
原文摘要 · Abstract (English)
The patterns of inhalation and exhalation contain important physiological signals that can be used to anticipate human behavior, health trends, and vital parameters. Human activity recognition (HAR) is fundamentally connected to these vital signs, providing deeper insights into well-being and enabling real-time health monitoring. This work presents i-Mask, a novel HAR approach that leverages exhaled breath patterns captured using a custom-developed mask equipped with integrated sensors. Data collected from volunteers wearing the mask undergoes noise filtering, time-series decomposition, and labeling to train predictive models. Our experimental results validate the effectiveness of the approach, achieving over 95\% accuracy and highlighting its potential in healthcare and fitness applications.
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