Cough Event Recognition Using Signal-Processing Based Feature Sets and Machine Learning, with Tri-Axial Accelerometer Sensor Worn at Multiple Body Points

Doddabasappla Basavarajappa, Kruthi

Cough Event Recognition Using Signal-Processing Based Feature Sets and Machine Learning, with Tri-Axial Accelerometer Sensor Worn at Multiple Body Points

dc.contributor.advisor	Vyas, Rushi J.
dc.contributor.author	Doddabasappla Basavarajappa, Kruthi
dc.contributor.committeemember	Murari, Kartikeya
dc.contributor.committeemember	Yanushkevich, Svetlana N
dc.contributor.committeemember	Medeiros de Souza, Roberto
dc.date	Winter Conferral
dc.date.accessioned	2023-02-11T00:31:16Z
dc.date.embargolift	2023-02-22
dc.date.issued	2021-12-22
dc.description.abstract	Human activity recognition (HAR) from time-series accelerometer and gyroscope sensor data has seen tremendous progress in recent years. Laying, standing, sitting, walking, walking down, and walking upstairs are the daily human activities that are commonly classified using sensor data. Cough is a common human activity and is also a symptom of various diseases including the novel coronavirus disease 2019 (COVID-19). Cough detection and classification are well investigated in recent literature to varying levels of success. But, in most of the studies, sensor data for cough activity is collected during still conditions such as sitting or standing and from a specific location such as chest and neck only. The body position of data recording considerably impacts the data, significantly affecting the classification accuracy. In our study, we place tri-axial accelerometer sensors at different spots on the human body where a smartphone or wearable device such as earphones or headphones are commonly worn. We studied the data with statistical and machine learning (ML) based signal processing methods to find the best accelerometer sensor position to detect coughing events accurately on the human body. Our study finds the most suitable sensor position for cough recognition considering the noise introduced by walking and considering different human heights. The proposed multi-band frequency-domain features such as Spectral Summation, Spectral Maximum, and Spectral Spread of acceleration signal offer higher classification accuracy for cough activity.
dc.identifier.citation	Doddabasappla Basavarajappa, K. (2021). Cough Event Recognition Using Signal-Processing Based Feature Sets and Machine Learning, with Tri-Axial Accelerometer Sensor Worn at Multiple Body Points (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	http://hdl.handle.net/1880/115828
dc.identifier.uri	https://dx.doi.org/10.11575/PRISM/40722
dc.language.iso	en	en
dc.language.iso	English
dc.publisher.faculty	Graduate Studies	en
dc.publisher.faculty	Schulich School of Engineering
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en
dc.subject	Machine Learning
dc.subject	Convolution Neural Network
dc.subject	Accelerometer
dc.subject	Cough
dc.subject	Human Activity Recognition
dc.subject	Sensor
dc.subject	Signal processing
dc.subject	Frequency spectrum
dc.subject	Wearable technology
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Engineering--Electronics and Electrical
dc.subject.classification	Computer Science
dc.title	Cough Event Recognition Using Signal-Processing Based Feature Sets and Machine Learning, with Tri-Axial Accelerometer Sensor Worn at Multiple Body Points
dc.type	master thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary	en
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)