BreathMentor

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is currently the third major cause of death--more than three million people died from it in 2019. Given that COPD cannot be cured currently, immediate treatment is crucial. Pulmonary rehabilitation (PR) is widely used to prevent COPD deterioration. Patients are advised to undergo a PR at home to get sufficient treatment in time. Monitoring patients during home rehabilitation can help not only improve patient adherence but also collect data on patients' recovery progress from rehabilitation team's perspective. However, how to track if proper diaphragmatic breathing, an essential part of PR, is taken by a patient has remained challenging. The current monitoring solution still appears obtrusive as it requires the patient to wear two uncomfortable respiration belts. Alternatively, therapists need to monitor the patients remotely through several cameras, which consumes substantial medical resources and causes privacy issues. In this work, we present BreathMentor, a smart speaker based diaphragmatic breathing monitoring system targeting early COPD stages I and II. BreathMentor is both unobtrusive and preventive of privacy invasion, so that it can solve the existing pain points and suits home care. BreathMentor converts the smart speaker into an active sonar system that continuously perceives and analyses the changes in surroundings, thereby detecting the user's respiration rate, deriving the breathing phases, and classifying whether the patient is practising diaphragmatic breathing. BreathMentor formulates breathing monitoring as a Temporal Action Localization task that enables us to detect each breathing cycle and classify its type. Our key insight is that breathing periodicity and phase duration are natural properties to localize and segment the breaths. Our key design to classify the breathing type is a hybrid architecture encompassing signal processing and deep learning techniques. Further, we evaluate the system performance on fifteen healthy subjects who would not breathe abnormally during diaphragmatic breathing under the supervision of therapists. In conclusion, BreathMentor can achieve robust performance for monitoring diaphragmatic breathing in different environments, as demonstrated in the results. The median error rate of respiration detection is 0.2 BPM, and the I/E ratio derivation is accurate with a mean absolute percentage error of less than 5.9% for breathing phase detection, together with a recall of 98.2%, and a precision of 95.5% in detecting diaphragmatic breathing. Above results indicate that BreathMentor can be used to track the patients' adherence and help monitor their breathing capacity.