This study investigates the potential of using smartwatches with built-in microphone sensors for monitoring coughs and detecting various cough types. We conducted a study involving 32 participants and collected 9 hours of audio data in a controlled manner. Afterward, we processed this data using a structured approach, resulting in 223 positive cough samples. We further improved the dataset through augmentation techniques and employed a specialized 1D CNN model. This model achieved an impressive accuracy rate of 98.49% while non-walking and 98.2% while walking, showing smartwatches can detect cough. Moreover, our research successfully identified four distinct types of coughs using clustering techniques.

本研究探讨了使用内置麦克风传感器的智能手表来监测咳嗽并检测各种咳嗽类型的潜力。我们对32名参与者进行了一项研究，并以受控方式收集了9小时的音频数据。随后，我们使用结构化方法处理了这些数据，得到了223个阳性咳嗽样本。我们进一步通过增加技术改进了数据集，并采用了专门的1D CNN 模型。该模型在非步行状态下的准确率达到了98.49%，步行状态下达到了98.2%，证明了智能手表可以检测到咳嗽。此外，我们的研究成功使用聚类技术识别出了四种不同类型的咳嗽。

利用智能手表麦克风传感器进行咳嗽检测与分类