scholarly journals A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study

10.2196/22753 ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. e22753
Author(s):  
Chuan Yang ◽  
Wei Zhang ◽  
Zhixuan Pang ◽  
Jing Zhang ◽  
Deling Zou ◽  
...  
Keyword(s):  
Real Time ◽  
Heart Diseases ◽  
Low Cost ◽  
Pearson Correlation ◽  
Raspberry Pi ◽  
Respiratory Sounds ◽  
Respiratory Dysfunction ◽  
Electronic Stethoscope ◽  
Prototype Development ◽  
Amplitude Data

Background Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients. Objective The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope. Methods We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope. Results We were able to conveniently operate Auscul Pi and precisely record the patients’ auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001). Conclusions Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. Trial Registration ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971.

scholarly journals Development of a Low-Cost Ear-Contactless Stethoscope Powered by Raspberry Pi: A Solution to the Auscultation Difficulty on Patients with COVID-19 (Preprint)

2020 ◽  
Author(s):  
Chuan Yang ◽  
Wei Zhang ◽  
Zhixuan Pang ◽  
Jing Zhang ◽  
Deling Zou ◽  
...  
Keyword(s):  
Real Time ◽  
Safety Concern ◽  
Low Cost ◽  
Severe Pneumonia ◽  
Heart Sounds ◽  
Raspberry Pi ◽  
Respiratory Dysfunction ◽  
Electronic Stethoscope ◽  
Breath Sounds ◽  
Data Files

BACKGROUND In the battle against COVID-19, auscultation examination was essential, especially to patients with poor respiratory conditions, such as severe pneumonia, respiratory dysfunction, and intensive cases who were intubated and assisted with ventilators. However, auscultation was hard to be accomplished on the infected patients due to the safety concern and unavailability for medical workers wearing personal protective suits. OBJECTIVE The objective of our study was to design and develop an electronic stethoscope with the characteristics of ear-contactless auscultation, low-cost property, and digital storage for further analysis. An assessment of its clinical feasibility should also be made with the comparison to the electronic stethoscope currently in use. METHODS We developed a prototype of the electronic stethoscope without ear-contact, Auscul Pi, powered by Raspberry Pi and Python, which can make real-time auscultation sounds played with a micro-speaker instead of ear pieces, and it can also store data files for further analysis. We utilized this stethoscope to assess the feasibility by detecting abnormal heart and breath sounds from 8 patients by comparing it with 3M Littmann electronic stethoscope, and 2 healthy volunteers were included for controls. We then plotted the phonocardiography of heart sounds for visualization for the comparisons. RESULTS We were able to operate Auscul Pi conveniently and record the auscultation sounds precisely in practice from the aspects of ergonomics and information technology. A total of 10 participants were recruited to receive auscultation examination with Auscul Pi and 3M Littmann electronic stethoscope. In terms of the real-time playout and recorded audio of heart sounds and breath sounds, the Auscul Pi showed consistency to 3M Littmann. As for the heart sounds, we also plotted phonocardiograph based on the data generated by Auscul Pi and 3M Littmann, and aligned them with the cardiac cycle of ECG respectively. The phonocardiography showed good conformity between Auscul Pi and 3M Littmann according to the waveforms. CONCLUSIONS Auscul Pi is feasible to perform the auscultation in clinical practice by applying real-time ear-contactless playout and later quantified analysis of auscultation sounds. So, it is expected to benefit the patients with COVID-19 examined by medical employees wearing protective suits and having difficulties in auscultation. CLINICALTRIAL ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971

scholarly journals A Portable Fuzzy Driver Drowsiness Estimation System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4093
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Marley Vellasco ◽  
René González
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Raspberry Pi ◽  
Inference System ◽  
Visual Occlusion ◽  
Computational Performance ◽  
Estimation System ◽  
Definition Of

The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drivers, and robust to conditions such as illumination changes or visual occlusion. With the advent of a new generation of computationally powerful embedded systems such as the Raspberry Pi, a new category of real-time and low-cost portable drowsiness detection systems could become standard tools. Usually, the proposed solutions using this platform are limited to the definition of thresholds for some defined drowsiness indicator or the application of computationally expensive classification models that limits their use in real-time. In this research, we propose the development of a new portable, low-cost, accurate, and robust drowsiness recognition device. The proposed device combines complementary drowsiness measures derived from a temporal window of eyes (PERCLOS, ECD) and mouth (AOT) states through a fuzzy inference system deployed in a Raspberry Pi with the capability of real-time response. The system provides three degrees of drowsiness (Low-Normal State, Medium-Drowsy State, and High-Severe Drowsiness State), and was assessed in terms of its computational performance and efficiency, resulting in a significant accuracy of 95.5% in state recognition that demonstrates the feasibility of the approach.

OBD-II and raspberry Pi technology to diagnose car’s machine current condition: study literature

2017 ◽  
Vol 34 (10) ◽  
pp. 15-21 ◽  
Author(s):  
Sonya Rapinta Manalu ◽  
Jurike Moniaga ◽  
Dionisius Andrian Hadipurnawan ◽  
Firda Sahidi
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Design Methodology ◽  
Low Cost ◽  
Raspberry Pi ◽  
Data Link ◽  
Content Type ◽  
History Of ◽  
To Receive ◽  
Technology Comparison

Purpose Low-cost microcomputers such as the Raspberry Pi are common in library makerspaces. This paper aims to create an OBD-II technology to diagnose a vehicle’s condition. Design/methodology/approach An OBD-II scanner plugged into the OBD-II port or usually called the data link connector (DLC), sends diagnostics to the Raspberry Pi. Findings Compared with other microcontrollers such as Arduino, the Raspberry Pi was chosen because it sustains the application to receive real-time diagnostics, process the diagnostics and send commands to automobiles at the same time, rather than Arduino that must wait for another process finished to run another process. Originality/value This paper also represents the history of mobile technology and OBD-II technology, comparison between Arduino and Raspberry Pi and Node.

scholarly journals Artificial vision system for object classification in real time using Raspberry Pi and a Web camera

2021 ◽  
pp. 20-25
Author(s):  
Tomás Serrano-Ramírez ◽  
Ninfa del Carmen Lozano-Rincón ◽  
Arturo Mandujano-Nava ◽  
Yosafat Jetsemaní Sámano-Flores
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Vision System ◽  
Low Cost ◽  
Artificial Vision ◽  
Maintenance Cost ◽  
Raspberry Pi ◽  
Smart Cameras ◽  
Suggested Technique ◽  
Web Camera

Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

scholarly journals Raspberry Pi for Commercial Applications

2013 ◽  
Vol 11 (2) ◽  
pp. 2250-2255 ◽  
Author(s):  
Chaitanya Bysani ◽  
T. S. Rama Krishna Prasad ◽  
Sridhar Chundi
Keyword(s):  
User Interface ◽  
Real Time ◽  
Low Cost ◽  
Raspberry Pi ◽  
Video Playback ◽  
Automotive Safety ◽  
Internet Connectivity ◽  
Connected Network ◽  
Commercial Off The Shelf ◽  
Commercial Applications

The objective of this paper is to create a low cost commercial off the shelf data analyzer for improving automotive safety and design a user interface infotainment system by using Raspberry Pi.  In this paper we propose Raspberry pi based application that monitor the vehicle ECUs through an OBD-II(On Board Diagnostics) interface, perform Diagnostics with DTCs (Diagnostics trouble codes). Infotainment system having functions such as audio and video playback, games, internet connectivity through either USB Wi-Fi dongles or USB Modems and dashboard camera operation. Raspberry Pi will transmit the data over Wi-Fi in real-time in xml format over Wi-Fi on a DHCP connected network.

scholarly journals Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

2020 ◽  
Vol 10 (21) ◽  
pp. 7448
Author(s):  
Jorge Felipe Gaviria ◽  
Alejandra Escalante-Perez ◽  
Juan Camilo Castiblanco ◽  
Nicolas Vergara ◽  
Valentina Parra-Garces ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Urban Areas ◽  
Low Cost ◽  
Raspberry Pi ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Main Challenge

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

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