We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient’s bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients. Logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) classifiers provide a baseline and are compared with three deep architectures, convolutional neural network (CNN), long short-term memory (LSTM) network, and residual-based architecture (Resnet50) using a leave-one-out cross-validation scheme. We find that it is possible to use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance, achieving an area under the ROC curve (AUC) exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.

我们提出了一种基于加速度计和音频信号的自动无创检测咳嗽事件的方法。加速度信号由牢固连接在患者床上的智能手机使用其集成加速度计捕获。同一部智能手机使用外部麦克风同时捕获音频信号。我们已经编译了一个手动注释的数据集，其中包含来自 14 名成年男性患者的大约 6000 次咳嗽和 68000 次非咳嗽事件的同时捕获的加速度和音频信号。逻辑回归 (LR)、支持向量机 (SVM) 和多层感知器 (MLP) 分类器提供基线，并与三种深度架构进行比较，卷积神经网络 (CNN)、长短期记忆 (LSTM) 网络、和使用留一法交叉验证方案的基于残差的架构（Resnet50）。我们发现可以使用加速度或音频信号以高精度区分咳嗽和其他活动，包括打喷嚏、清喉咙和床上运动。然而，在所有情况下，深度神经网络都明显优于浅层分类器，Resnet50 提供了最佳性能，加速和音频信号的 ROC 曲线下面积 (AUC) 分别超过 0.98 和 0.99。虽然基于音频的分类始终提供比基于加速的分类更好的性能，但我们观察到对于最佳系统来说差异非常小。由于加速度信号需要较少的处理能力，