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  1. Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients’ privacy by obfuscating their speech, and we analyze the trade-offmore »between speech obfuscation for privacy and cough detection accuracy. Clinical relevance—This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].« less
  2. Multi-modal wearable sensors monitoring physiology and environment simultaneously would offer a great promise to manage respiratory health, especially for asthmatic patients. In this study, we present a preliminary investigation of the correlation between ozone exposure, heart rate, heart rate variability, and lung function. As the first step, we tested the effect of low-level ozone exposure in a sample size of four healthy individuals. Test subjects underwent controlled exposure from 0.06 to 0.08 ppm of ozone and filtered air on two separate exposure days. Our results indicate an increment in mean heart rate in three out of four test subjects when exposed to ozone. We have also observed that changes in mean heart rate has a positive correlation with changes in lung function and a negative correlation with changes in neutrophil count. These results provide a baseline understanding of healthy subjects as a control group.