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1. BreathTrack: Detecting Regular Breathing Phases from Unannotated Acoustic Data Captured by a Smartphone

   https://doi.org/10.1145/3478123  

   Islam, Bashima; Rahman, Md Mahbubur; Ahmed, Tousif; Ahmed, Mohsin Yusuf; Hasan, Md Mehedi; Nathan, Viswam; Vatanparvar, Korosh; Nemati, Ebrahim; Kuang, Jilong; Gao, Jun Alex (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   null (Ed.)

   Breathing biomarkers, such as breathing rate, fractional inspiratory time, and inhalation-exhalation ratio, are vital for monitoring the user's health and well-being. Accurate estimation of such biomarkers requires breathing phase detection, i.e., inhalation and exhalation. However, traditional breathing phase monitoring relies on uncomfortable equipment, e.g., chestbands. Smartphone acoustic sensors have shown promising results for passive breathing monitoring during sleep or guided breathing. However, detecting breathing phases using acoustic data can be challenging for various reasons. One of the major obstacles is the complexity of annotating breathing sounds due to inaudible parts in regular breathing and background noises. This paper assesses the potential of using smartphone acoustic sensors for passive unguided breathing phase monitoring in a natural environment. We address the annotation challenges by developing a novel variant of the teacher-student training method for transferring knowledge from an inertial sensor to an acoustic sensor, eliminating the need for manual breathing sound annotation by fusing signal processing with deep learning techniques. We train and evaluate our model on the breathing data collected from 131 subjects, including healthy individuals and respiratory patients. Experimental results show that our model can detect breathing phases with 77.33% accuracy using acoustic sensors. We further present an example use-case of breathing phase-detection by first estimating the biomarkers from the estimated breathing phases and then using these biomarkers for pulmonary patient detection. Using the detected breathing phases, we can estimate fractional inspiratory time with 92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the breathing rate with 91.74% accuracy. Moreover, we can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory health and well-being using acoustic data captured by a smartphone. 

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2. Extracting Individual Respiratory Signatures from Combined Multi-Subject Mixtures with Varied Breathing Pattern Using Independent Component Analysis with the JADE Algorithm

   https://doi.org/10.1109/APMC47863.2020.9331715  

   Islam, Shekh M.; Lubecke, Victor M. (December 2020, 2020 IEEE Asia-Pacific Microwave Conference (APMC))

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   Concurrent respiration monitoring of multiple subjects remains a challenge in microwave Doppler radar-based non-contact physiological sensing technology. Prior research using Independent component analysis with the JADE algorithm has been limited to the separation of respiratory signatures for normal breathing patterns. This paper investigates the feasibility of using the ICA-JADE algorithm with a 24-GHz phase comparison monopulse radar transceiver for separating respiratory signatures from combined mixtures of varied breathing patterns. Normal, fast, and slow breathing pattern variations likely to occur due to physiological activity, and emotional stress were used as a basis for assessing separation robustness. Experimental results showed efficacy for recognition of three different breathing patterns, and isolation of respiratory signatures with an accuracy of100% for normal breathing, 92% for slow breathing, and 83.78% for fast breathing using ICA-JADE. Breathing pattern variations were observed to affect the signal-to-noise ratio through multiple mechanisms, decreasing with an increase in the number of breathing cycles and associated motion artifacts. Additionally, for removing motion artifacts of fast breathing pattern empirical mode decomposition (EMD) is employed, and for slow breathing pattern, increasing the breathing cycles helps to achieve an accuracy of 89.2% and 94.5% respectively. 

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3. BuMA: Non-Intrusive Breathing Detection using Microphone Array

   https://doi.org/10.1145/3539490.3539598  

   Hou, Kaiyuan; Xia, Stephen; Jiang, Xiaofan (June 2022, Proceedings of the 1st ACM International Workshop on Intelligent Acoustic Systems and Applications (IASA))

   Breath monitoring is important for monitoring illnesses, such as sleep apnea, for people of all ages. One cause of concern for parents is sudden infant death syndrome (SIDS), where an infant suddenly passes away during sleep, usually due to complications in breathing. There are a variety of works and products on the market for monitoring breathing, especially for children and infants. Many of these are wearables that require you to attach an accessory onto the child or person, which can be uncomfortable. Other solutions utilize a camera, which can be privacy-intrusive and function poorly during the night, when lighting is poor. In this work, we introduce BuMA, an audio-based, non-intrusive, and contactless, breathing monitoring system. BuMA utilizes a microphone array, beamforming, and audio filtering to enhance the sounds of breathing by filtering out several common noises in or near home environments, such as construction, speech, and music, that could make detection difficult. We show that BuMA improves breathing detection accuracy by up to 12%, within 30cm from a person, over existing audio filtering algorithms or platforms that do not leverage filtering. 

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4. Self‐Powered Multifunctional Human–Machine Interfaces for Respiratory Monitoring and Smart System Control

   https://doi.org/10.1002/admi.202201202  

   Pang, Yaokun; Chen, Shoue; Cao, Yunteng; Huang, Zhida; Xu, Xianchen; Fang, Yuhui; Chase_Cao, Changyong (July 2022, Advanced Materials Interfaces)

   Abstract Innovative human–machine interfaces (HMIs) have attracted increasing attention in the field of system control and assistive devices for disabled people. Conventional HMIs that are designed based on the interaction of physical movements or language communication are not effective or appliable to severely disabled users. Here, a breath‐driven triboelectric sensor is reported consisting of a soft fixator and two circular‐shaped triboelectric nanogenerators (TENGs) for self‐powered respiratory monitoring and smart system control. The sensor device is capable of effectively detecting the breath variation and generates responsive electrical signals based on different breath patterns without affecting the normal respiration. A breathing‐driven HMI system is demonstrated for severely disabled people to control electrical household appliances and shows an intelligent respiration monitoring system for emergence alarm. The new system provides the advantages of high sensitivity, good stability, low cost, and ease of use. This work will not only expand the development of the TENGs in self‐powered sensors, but also opens a new avenue to develop assistive devices for disabled people through innovation of advanced HMIs. 

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5. Respiratory Feature Extraction for Radar-Based Continuous Identity Authentication

   https://doi.org/10.1109/RWS45077.2020.9050013  

   Islam, Shekh M; Sylvester, Abraham; Orpilla, George; Lubecke, Victor M. (January 2020, 2020 IEEE Radio and Wireless Symposium (RWS))

   null (Ed.)

   Radar is an attractive approach for identity authentication because it requires no contact and is unobtrusive. Most reported results have focused only on sedentary breathing patterns, without considering how respiratory patterns may change due to physiological activities or emotional stress. In this research the feasibility of extracting identifying features from radar respiratory traces was tested, for sedentary subject conditions as well as just after performing physiological activities (walking upstairs). Respiratory breathing dynamics related features (breathing rate, spectral entropy, breathing depth, inhale/exhale area ratio, mean and standard deviation of the peaks) were extracted from radar captured respiration patterns, and variations in feature parameters after physiological activities were assessed. Experimental results demonstrated that, after short exertions dynamically segmented respiratory pattern exhale area and breathing depth increased by more than 1.4 times for all participants, which made evident the uniqueness of residual heart volume after expiration for recognizing each subject even after short exertions. Our proposed approach is also integrated with a Support Vector Machine (SVM) with a radial basis function kernel to demonstrate an identification success rate of almost 98.55% for sedentary-only conditions and almost 92% for a combined mixture of conditions (sedentary and after short exertion). While the efficacy was reduced, the method still shows significant potential. The proposed identity authentication approach has several potential applications including security/surveillance, IOT applications, virtual reality and health monitoring as well. 

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