ECG monitoring using Android mobile phone and Bluetooth

Lucena, Samuel E. de; Sampaio, Daniel; Mall, Benjamin; Meyer, Max; Burkart, Martin A.; Keller, Frieder V.

doi:10.1109/i2mtc.2015.7151585

Cited by 19 publications

(7 citation statements)

References 5 publications

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“…For example, costs of state-of-the-art eye tracking equipment range between US$ 20,000 and US$ 30,000 (Ferhat and Vilariño, 2016) and EEG equipment can cost more than US$ 75,000 (Krigolson et al, 2017). Costs of sensors measuring peripheral physiology such as heart rate, respiration and EDA can also be high (Lucena et al, 2015), with a complete research system including hardware and software easily reaching US$ 10,000 per modality.…”

Section: Hardware and Software Expensesmentioning

confidence: 99%

“…Additionally, integrating sensors in smartphones, laptops, and tablets might also be an interesting endeavor, with for example advancements being made in using cameras of such devices for collecting eye-related measures (Lin et al, 2013;Lim et al, 2015;Krafka et al, 2016;Meißner et al, 2019) and measures of heart rate and heart rate variability (Heathers, 2013;Lucena et al, 2015).…”

Section: Embedding Sensors In Learning Systems and Widely Used Devicesmentioning

confidence: 99%

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Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

2020

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Although many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field. The findings of the SWOT analysis indicate that the field of neurophysiology in learning and training is developing rapidly. By leveraging the opportunities of neurophysiology in learning and training (while bearing in mind weaknesses, threats, and strengths) the field can move forward in promising directions. Suggestions for opportunities for future work are provided to ensure valid and effective application of non-invasive neurophysiology in a wide range of learning and training settings.

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Section: Hardware and Software Expensesmentioning

confidence: 99%

Section: Embedding Sensors In Learning Systems and Widely Used Devicesmentioning

confidence: 99%

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

2020

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“…Other prototypes have used a Peripheral Interface Controller (PIC) for ECG monitoring using an Android™ mobile phone and Bluetooth. In [ 9 ], one single lead was digitized with a 10-bit Analog-to-Digital Converter (ADC), processed in the PIC, and sent to the cellphone for viewing. Another basic ECG mobile system was recently implemented with the filtering and conditioning modules for a single lead with instrumentation and operational amplifiers [ 10 ].…”

Section: Previous Related Workmentioning

confidence: 99%

A Flexible 12-Lead/Holter Device with Compression Capabilities for Low-Bandwidth Mobile-ECG Telemedicine Applications

Pineda-Lopez

Martínez-Fernández

Rojo-Álvarez

et al. 2018

Sensors

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In recent years, a number of proposals for electrocardiogram (ECG) monitoring based on mobile systems have been delivered. We propose here an STM32F-microcontroller-based ECG mobile system providing both long-term (several weeks) Holter monitoring and 12-lead ECG recording, according to the clinical standard requirements for these kinds of recordings, which in addition can yield further digital compression at stages close to the acquisition. The system can be especially useful in rural areas of developing countries, where the lack of specialized medical personnel justifies the introduction of telecardiology services, and the limitations of coverage and bandwidth of cellular networks require the use of efficient signal compression systems. The prototype was implemented using a small architecture, with a 16-bits-per-sample resolution. We also used a low-noise instrumentation amplifier TI ADS1198, which has a multiplexer and an analog-to-digital converter (16 bits and 8 channels) connected to the STM32F processor, the architecture of which incorporates a digital signal processing unit and a floating-point unit. On the one hand, the system portability allows the user to take the prototype in her/his pocket and to perform an ECG examination, either in 12-lead controlled conditions or in Holter monitoring, according to the required clinical scenario. An app in the smartphone is responsible for giving the users a friendly interface to set up the system. On the other hand, electronic health recording of the patients are registered in a web application, which in turn allows them to connect to the Internet from their cellphones, and the ECG signals are then sent though a web server for subsequent and ubiquitous analysis by doctors at any convenient terminal device. In order to determine the quality of the received signals, system testing was performed in the three following scenarios: (1) The prototype was connected to the patient and the signals were subsequently stored; (2) the prototype was connected to the patient and the data were subsequently transferred to the cellphone; (3) the prototype was connected to the patient, and the data were transferred to the cellphone and to the web via the Internet. An additional benchmarking test with expert clinicians showed the clinical quality provided by the system. The proposed ECG system is the first step and paves the way toward mobile cardiac monitors in terms of compatibility with the electrocardiographic practice, including the long-term monitoring, the usability with 12 leads, and the possibility of incorporating signal compression at the early stages of the ECG acquisition.

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“…Eighteen articles published between the years 2016-2018 were included in the review [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] from conference or journal. Research originated from eleven different countries including Germany [9,10], Romania [11,12], India [13][14][15][16][17][18], Bangladesh [19], Iraq [20], Italy [21], Taiwan [22], Netherlands [23], Ecuador [24], Indonesia [25] and United Kingdom [26].…”

Section: Search Resultsmentioning

confidence: 99%

“…If the data is delivered quickly, handling actions will also be done quickly. The adoption of Internet of Things (IoT) technologies is the way to improve living conditions especially for the elderly using the development of sensor, internet, and Android-mobile [9]. Health monitoring system using IoT can utilize various sensors, microcontrollers, and user interface devices also provide secure, efficient, and intelligent services to applications [10].…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

Putri¹,

Ali²,

Saad³

et al. 2018

IJET

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E-health becomes one of the internet's products for healthcare. The problems of health service such as far hospital and expensive examination fees become the emergence of this technology. Consequently, people reluctant to check their health to hospital. E-health provides information on disease prevention, detecting early symptoms, and monitoring the patient's condition based on medical parameters from a far distance. Internet of things became the main concept in this system, which combines wearable sensors, communication systems, and mobile user interfaces. Reliable and valid system, easily carried, help the doctor to monitor patients from far distance expectantly to overcome the problems. The aims of this paper review are describing how an internet of things technology and wearable sensor help medical science and find the best way to create a health monitoring system.

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ECG monitoring using Android mobile phone and Bluetooth

Cited by 19 publications

References 5 publications

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

A Flexible 12-Lead/Holter Device with Compression Capabilities for Low-Bandwidth Mobile-ECG Telemedicine Applications

Wearable Sensor and Internet of Things Technology for Better Medical Science: A Review

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