Spectral Summation With Machine Learning Analysis of Tri-Axial Acceleration From Multiple Wearable Points on Human Body for Better Cough Detection

Abstract

In this letter, coughing is detected using a multiband spectral summation of acceleration signal with machine learning (ML) from various body points where smartphones are commonly worn. A known challenge in this letter is discerning low and mid intensity cough events from noise introduced by walking from the chest, stomach, shirt pocket, upper hand, and ear where smartphones are commonly worn from among seven test subjects of varying heights. Previous studies have shown that coughing during walking can be accurately detected with only 92, 73, 62, and 82% accuracy at the chest, stomach, shirt pocket, and upper hand, respectively, just from raw acceleration signals in the time domain and ML. Newer spectrum analysis show that acceleration measured at these body points along the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> -axis are more spectrally rich during low to medium coughing and walking activity than just walking alone in seven test subjects of varying heights. In this letter, we investigate the use multiband spectral summation features of acceleration measured at these same body points on the torso with ML to improve the accuracy of low/mid intensity cough detection to between 95.2 and 98.2%. At just the chest and upper hand, the spectral sum of acceleration in the 0–5 Hz band shows a 46–142% and 58–136% increase during coughing and walking than just walking alone for different cough intensities and subject heights. This letter is useful in developing future cough-detecting apps on smartphones commonly worn on the torso.