Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit

State-of-the-art respiration tracking devices require specialized equipment, making them impractical for every day at-home respiration sensing. In this paper, we present the first system for sensing respiratory rates using in-ear headphone inertial measurement units (IMU). The approach is based on technology already available in commodity devices: the eSense headphones. Our processing pipeline combines several existing approaches to clean noisy data and calculate respiratory rates on 20-second windows. In a study with twelve participants, we compare accelerometer and gyroscope based sensing and employ pressure-based measurement with nasal cannulas as ground truth. Our results indicate a mean absolute error of 2.62 CPM (acc) and 2.55 CPM (gyro). This overall accuracy is comparable to previous approaches using accelerometer-based sensing, but we observe a higher relative error for the gyroscope. In contrast to related work using other sensor positions, we can not report significant differences between the two modalities or the three postures standing, sitting, and lying on the back (supine). However, in general, performance varies drastically between participants

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Sudden deterioration of condition in patients with various diseases, such as cardiopulmonary arrest, may result in poor outcome even after resuscitation. Early detection of deterioration is important in medical and long-term care settings, regardless of the acute or chronic phase of disease. Early detection and appropriate interventions are essential before resuscitating measures are required. Among the vital signs that indicate the general condition of a patient, respiratory rate has a greater ability to predict serious events such as thromboembolism and sepsis than heart rate and blood pressure, even in early stages. Despite its importance, however, respiratory rate is frequently overlooked and not measured, making it a neglected vital sign. To facilitate the measurement of respiratory rate, a non-invasive method of detecting respiratory sounds was developed based on deep learning technology, using a built-in microphone in a smartphone. Smartphones attached to the bed headboards of 20 participants undergoing polysomnography (PSG) at Kyoto University Hospital recorded respiratory sounds. Sound data were synchronized with overnight respiratory information. After excluding periods of abnormal breathing on the PSG report, sound data were processed for each 1-minute period. Expiration sound was determined using the pressure flow sensor signal on PSG. Finally, a model to identify the expiration section from the sound information was created using a deep learning algorithm from the convolutional Long Short Term Memory network. The accuracy of the learning model in identifying the expiratory section was 0.791, indicating that respiratory rate can be determined using the microphone in a smartphone. By collecting data from more patients and improving the accuracy of this method, respiratory rates could be more easily monitored in all situations, both inside and outside the hospital

A Microcontroller Based System for Controlling Patient Respiratory Guidelines

The need of making improvements in obtaining (in a non-invasive way) and monitoring the breathing rate parameters in a patient emerges due to (1) the great amount of breathing problems our society suffer, (2) the problems that can be solved, and (3) the methods used so far. Non-specific machines are usually used to carry out these measures or simply calculate the number of inhalations and exhalations within a particular timeframe. These methods lack of effectiveness and precision thus, influencing the capacity of getting a good diagnosis. This proposal focuses on drawing up a technology composed of a mechanism and a user application which allows doctors to obtain the breathing rate parameters in a comfortable and concise way. In addition, such parameters are stored in a database for potential consultation as well as for the medical history of the patients. For this, the current approach takes into account the needs, the capacities, the expectations and the user motivations which have been compiled by means of open interviews, forum discussions, surveys and application uses. In addition, an empirical evaluation has been conducted with a set of volunteers. Results indicate that the proposed technology may reduce cost and improve the reliability of the diagnosis.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Novel Approach to Respiratory Rate Measurement Using Resonance Tube with Contradictory Thresholding Technique

In this paper, we propose a novel approach to respiratory rate measurement using resonance tube to enhance the performance of microphone inserted and fixed at the end of the tube to catch breath sound signal from the mouth and/or nose. The signal is amplified and passed into envelope detector circuit after which it is compared with a suitable reference voltage in comparator circuit to generate a pulse train of square wave synchronized with the respiratory cycle. A simple algorithm is developed in a small microcontroller to detect rising edges of each consecutive square wave to calculate respiratory rate together with analysis of breathing status. In order to evade noises which will cause errors and artifacts in the measuring system, the reference voltage is creatively designed to intelligently adapt itself to be low during expiration period and high during inspiration and pause period using the concept of resolving contradiction in the theory of inventive problem solving (TRIZ). This makes the developed device simple and low-cost with no need for complicated filtering system. It can detect breath sound as far as 250 cm from the nose and can perform accurately as tested against End Tidal CO2 Capnography device. The result shows that the developed device can estimate precisely from as low as 0 BrPM to as high as 98 BrPM and it can detect shallow breathing as low as 10 mV of breath sound

Ultra-Low-Power IoT Solutions for Sound Source Localization: Combining Mixed-Signal Processing and Machine Learning

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of auditory stimuli that could provide important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. We start this research into building a wearable system that uses multichannel audio sensors embedded in a headset to help detect and locate cars from their honks and engine and tire noises. Based on this detection, the system can warn pedestrians of the imminent danger of approaching cars. We demonstrate that using a segmented architecture and implementation consisting of headset-mounted audio sensors, front-end hardware that performs signal processing and feature extraction, and machine-learning-based classification on a smartphone, we are able to provide early danger detection in real time, from up to 80m distance, with greater than 80% precision and 90% recall, and alert the user on time (about 6s in advance for a car traveling at 30mph). The time delay between audio signals in a microphone array is the most important feature for sound-source localization. This work also presents a polarity-coincidence, adaptive time-delay estimation (PCC-ATDE) mixed-signal technique that uses 1-bit quantized signals and a negative-feedback architecture to directly determine the time delay between signals in the analog inputs and convert it to a digital number. This direct conversion, without a multibit ADC and further digital-signal processing, allows for ultra low power consumption. A prototype chip in 0:18μm CMOS with 4 analog inputs consumes 78nW with a 3-channel 8-bit digital time-delay output while sampling at 50kHz with a 20μs resolution and 6.06 ENOB. We present a theoretical analysis for the nonlinear, signal-dependent feedback loop of the PCC-ATDE. A delay-domain model of the system is developed to estimate the power bandwidth of the converter and predict its dynamic response. Results are validated with experiments using real-life stimuli, captured with a microphone array, that demonstrate the technique’s ability to localize a sound source. The chip is further integrated in an embedded platform and deployed as an audio-based vehicle-bearing IoT system. Finally, we investigate the signal’s envelope, an important feature for a host of applications enabled by machine-learning algorithms. Conventionally, the raw analog signal is digitized first, followed by feature extraction in the digital domain. This work presents an ultra-low-power envelope-to-digital converter (EDC) consisting of a passive switched-capacitor envelope detector and an inseparable successive approximation-register analog-to-digital converter (ADC). The two blocks integrate directly at different sampling rates without a buffer between them thanks to the ping-pong operation of their sampling capacitors. An EDC prototype was fabricated in 180nm CMOS. It provides 7.1 effective bits of ADC resolution and supports input signal bandwidth up to 5kHz and an envelope bandwidth up to 50Hz while consuming 9.6nW

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about deteriorating lung health conditions. This paper presents our system SpiroMask that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for continuous lung health monitoring. We evaluate our approach on 48 participants (including 14 with lung health issues) and find that we can estimate parameters such as lung volume and respiration rate within the approved error range by the American Thoracic Society (ATS). Further, we show that our approach is robust to sensor placement inside the mask.Comment: Accepted in the ACM Transactions on Computing for Healthcare (HEALTH

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

Breathing pattern interpretation as an alternative and effective voice communication solution

Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the outside world by means of a unidirectional microphone and researches breathing-pattern interpretation (BPI) to encode messages in an interactive manner with minimal training. We present BP processing work with (1) output synthesized machine-spoken words (SMSW) along with single-channel Weiner filtering (WF) for signal de-noising, and (2) k-nearest neighbor (k-NN) classification of BPs associated with embedded dynamic time warping (DTW). An approved protocol to collect analogue modulated BP sets belonging to 4 distinct classes with 10 training BPs per class and 5 live BPs per class was implemented with 23 healthy subjects. An 86% accuracy of k-NN classification was obtained with decreasing error rates of 17%, 14%, and 11% for the live classifications of classes 2, 3, and 4, respectively. The results express a systematic reliability of 89% with increased familiarity. The outcomes from the current AAC setup recommend a durable engineering solution directly beneficial to the sufferers

IoT Platform for COVID-19 Prevention and Control: A Survey

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.Comment: 12 pages; Submitted to IEEE Internet of Things Journa