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.

翻译：本研究探讨了利用内置麦克风传感器的智能手表监测咳嗽及识别不同咳嗽类型的潜力。我们开展了一项涉及32名参与者的研究，在受控环境下收集了9小时音频数据。随后采用结构化方法处理数据，获得了223个阳性咳嗽样本。通过数据增强技术进一步优化数据集，并采用专门的一维CNN模型。该模型在非行走状态下达到98.49%的准确率，行走状态下为98.2%，证明了智能手表对咳嗽检测的可行性。此外，本研究通过聚类技术成功识别出四种不同类型的咳嗽。