FFT Spectrum Spread With Machine Learning (ML) Analysis of Triaxial Acceleration From Shirt Pocket and Torso for Sensing Coughs While Walking

Early detection of respiratory distress, marked by coughing associated with pandemics such as Covid, severe acute respiratory syndrome, and influenza, has become important for early public health preparedness. Recognizing respiratory distress from data pooled from accelerometers and other sensors co...

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Bibliographic Details
Published in:IEEE sensors letters 2022-01, Vol.6 (1), p.1-4
Main Authors: Vyas, Rushi, Doddabasappla, Kruthi
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Cough
Harmonic analysis
human activity recognition
Influenza
kNN
Legged locomotion
Machine learning
machine learning (ML)
Public health
Respiratory diseases
sensor
Sensor applications
Sensors
Severe acute respiratory syndrome
smart phone
Spectra
Stomach
Support vector machines
SVM
Time-domain analysis
Torso
Walking
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Summary:Early detection of respiratory distress, marked by coughing associated with pandemics such as Covid, severe acute respiratory syndrome, and influenza, has become important for early public health preparedness. Recognizing respiratory distress from data pooled from accelerometers and other sensors common in phones/wearables can be a useful tool in tracking diseases in larger populations. However, detecting low-/medium-intensity coughs, which are a precursor to influenza/Covid, are harder to detect in the presence of human activity especially walking. In this letter, we study spectrum-spread features of triaxial accelerometer signals measured from the human torso during coughs. In particular, we analyze the vestigial sideband like spurs that cough-induced motion of the torso produces alongside walking signal between 0.2 and 2 Hz and propose the use of its spectral spread square metric in discerning coughs during walking action in test subjects of different sizes. Unlike prior works on time-domain measurements or spectral summation (units: g) in multiple bands, this work uses bandwidth, i.e., spectrum-spread features of acceleration signals (units: Hz 2 ) to detect low to medium intensity coughs from a single accelerometer worn on the chest or shirt pocket or stomach. Acceleration signals measured at these points in five test subjects of varying heights, age, and weight show its median square spectral spread increase prominently along Y (up-down) and Z axes (front-back) from between 0.016-0.0167 Hz 2 to between 0.023-0.026 Hz 2 with a cough-detection threshold observed at 0.02 Hz 2 for all axes. Using a machine learning (ML) classification model with these spectral spread features results in cough detection accuracy of 92.5, 92.2, and 91.5% with k-nearest neighbors (kNN), and 94.3, 96.1, and 93.6% using Support Vector Machine (SVM) ML models for all three torso points especially shirt pocket where phones are commonly worn.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2021.3133887