Android-based Mobile Application for Home-based Electrocardiogram Monitoring Device with Google Technology and Bluetooth Wireless Communication

Goh, Voon Hueh; Hau, Yuan Wen

doi:10.1109/iecbes.2018.8626603

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…2) System 2: As shown in Figure 8, these systems use real-time detection and diagnosis of the device itself. Examples of such devices include smartwatches, smartphones, Nuvant Corventis PiiX, AliveCor, SmartCardia INYU and MyThrob System on Chip (SoC) in which the R-peaks are detected using Relative-Energy-based WeArable R-Peak Detection (REWARD) [125], and wavelet transform [126], [127], ST-segments using SVM [128], and the QRS complex is detected using WT [129]. Diagnosis is performed on the smart device itself using appropriate classification methods.…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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“…Recently a study presented mobile applications for an inhouse designed home-based heart screening device that integrates with Google technology [13]. By using Firebase Authentication and Firebase Storage provided b Google technology, it allows admin to have full control over database management.…”

Section: Related Workmentioning

confidence: 99%

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

Sulla-Torres¹,

Gutierrez-Quintanilla²,

Pinto-Rodriguez³

et al. 2020

IJACSA

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One of the most critical bone diseases is osteoporosis, which can be evaluated through measurements of bone mineral density. Though there is a lot of commercialized portable about health in general, few are oriented towards bone health and with a lack of user-friendly interface and data management system. This paper presents a mobile application development for the calculation of the Bone Mineral density, which integrates with Google technology. The Mobile-D methodology for the development of mobile applications, due to the sequentially in the processes or stages is used. Bone mineral density (BMD) was calculated using anthropometric regression equations, and an Android-based Mobile Application with Google technology was developed. By using Firebase Authentication and Firebase Storage provided by Google technology, it allows admin to have full control over database management. In short, this mobile application allows the calculation of the BMD of the students and data storage and data uploading to cloud storage for post-processing, online data management system with user authentication. In addition, the Internacional Organization for Standardization / International Electrotechnical Commission (ISO/IEC) 25022 standard was used to evaluate the quality in use of the mobile app, resulting in 93% of quality in use, this app being able to be used by health professionals for better decision-making.

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“…Our equipment parameters will be entered through a mobile device with an Android operating system [22]. In the current market, we have several programs for the development of these applications; the MIT App Inventor 2 software [23] contains two parts of programming: the first dedicated specifically to the graphic design of the user interface, and the second refers clearly to programming, this programming is by blocks of essential functions that are based on the C ++ language.…”

Section: Mobile Application For Equipment Controlmentioning

confidence: 99%

Design of an Electro-Stimulator Controlled by Bluetooth for the Improvement of Muscular Atrophy

Achata¹,

Sulla²,

Carlos³

et al. 2020

IJACSA

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Muscle stimulation consists of a muscle's work when it is in constant exercise or contraction. This article presents an electro-stimulator design that generates electrical pulses in muscle cells through two electrodes positioned in an area of the body, causing the response of said cells to improve muscle atrophy. The steps that were followed were the construction of the block diagram, the design, and development of the circuit, the design and development of the control based on the pic12F683 and pic 16F690 microcontrollers, finally, the development of the software of the mobile application that controls the equipment using Bluetooth signals, based on the standard of IEC 60601-1 for basic safety and essential equipment performance. It was possible to obtain control of the frequency, application time, and amplitude of the duty cycle to have better results when applying therapy in specific areas of the body through the mobile application. Finally, the design is developed to respond to the user's parameters, using the Bluetooth of a mobile device and allowing the generation of electrical pulses in the muscle cells to improve muscle atrophy. The team can be part of therapeutic sessions for people with quadriplegia, improving the physiotherapy sessions performed on patients.

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Android-based Mobile Application for Home-based Electrocardiogram Monitoring Device with Google Technology and Bluetooth Wireless Communication

Cited by 9 publications

References 3 publications

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

Design of an Electro-Stimulator Controlled by Bluetooth for the Improvement of Muscular Atrophy

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