scholarly journals Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

2021 ◽  
Author(s):  
Bardia Baraeinejad ◽  
Masood Fallah Shayan ◽  
Amir Reza Vazifeh ◽  
Diba Rashidi ◽  
Mohammad Saberi Hamedani ◽  
...  
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Detection Algorithm ◽  
Battery Life ◽  
Signal Loss ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Ultra Low Power ◽  
Arrhythmia Detection ◽  
New Device

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>

scholarly journals Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

2021 ◽  
Author(s):  
Bardia Baraeinejad
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Detection Algorithm ◽  
Battery Life ◽  
Signal Loss ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Ultra Low Power ◽  
Arrhythmia Detection ◽  
New Device

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>

scholarly journals Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

2021 ◽  
Author(s):  
Bardia Baraeinejad
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Detection Algorithm ◽  
Battery Life ◽  
Signal Loss ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Ultra Low Power ◽  
Arrhythmia Detection ◽  
New Device

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>

scholarly journals Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

2021 ◽  
Author(s):  
Bardia Baraeinejad ◽  
Masood Fallah Shayan ◽  
Amir Reza Vazifeh ◽  
Diba Rashidi ◽  
Mohammad Saberi Hamedani ◽  
...  
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Detection Algorithm ◽  
Battery Life ◽  
Signal Loss ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Ultra Low Power ◽  
Arrhythmia Detection ◽  
New Device

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>

scholarly journals Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform

2021 ◽  
Author(s):  
Bardia Baraeinejad ◽  
Masood Fallah Shayan ◽  
Amir Reza Vazifeh ◽  
Diba Rashidi ◽  
Mohammad Saberi Hamedani ◽  
...  
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Detection Algorithm ◽  
Battery Life ◽  
Signal Loss ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Ultra Low Power ◽  
Arrhythmia Detection ◽  
New Device

<p>This paper reports a new device for electrocardiogram (ECG) signal monitoring and software for signal analysis and artificial intelligence (AI) assisted diagnosis. </p> <p>The hardware mitigates the signal loss common in previous products by enhancing the ergonomy, flexibility, and battery life. The power efficiency is optimized by design using switching converters, ultra-low-power components, and efficient signal processing. It enables 14-day of uninterrupted ECG monitoring and connectivity with a smartphone and microSD card storage.</p><p>The software is implemented in Android app and web-based platforms via Internet of Things (IoT). This component provides cloud-based and local storage and uses AI for arrhythmia detection. The arrhythmia detection algorithm shows 98.7% accuracy using Artificial Neural Network and K-Nearest Neighbors methods, and 98.1% using Decision Tree method on test data set.</p>

scholarly journals Rural Healthcare IoT Architecture Based on Low-Energy LoRa

2021 ◽  
Vol 18 (14) ◽  
pp. 7660
Author(s):  
Ace Dimitrievski ◽  
Sonja Filiposka ◽  
Francisco José Melero ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
...  
Keyword(s):  
Low Power ◽  
Rural Areas ◽  
Urban Areas ◽  
Well Being ◽  
Battery Life ◽  
Ultra Low Power ◽  
Term Operation ◽  
Doctor Patient Communication ◽  
Connected Health ◽  
Iot Devices

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

scholarly journals Ultra low power mixer with out-of-band RF energy harvesting for wireless sensor networks applications

2020 ◽  
Vol 40 (1) ◽  
pp. 1-6
Author(s):  
Jie Jin ◽  
Xianming Wu ◽  
Zhijun Li
Keyword(s):  
Energy Harvesting ◽  
Low Power ◽  
Wireless Sensors ◽  
Supply Voltage ◽  
Battery Life ◽  
Ic Design ◽  
Rf Energy Harvesting ◽  
Ultra Low Power ◽  
Rf Energy ◽  
Cmos Rf

An ultra low power mixer with out-of-band radio frequency (RF) energy harvesting suitable for the wireless sensors network (WSN) application is proposed in this paper. The presented mixer is able to harvest the out-of-band RF energy and keep it working in ultra low power condition and extend the battery life of the WSN. The mixer is designed and simulated with Global Foundries ’ 0.18 μ m CMOS RF process, and it operates at 2.4GHz industrial, scientific, and medical (ISM) band. The Cadence IC Design Tools post-layout simulation results demonstrate that the proposed mixer consumes 248 μ W from a 1V supply voltage. Furthermore, the power consumption can be reduced to 120.8 μ W by the out-of-band RF energy harvesting rectifier.

scholarly journals Battery life optimization techniques for ultra-low power SOCs

2019 ◽  
Vol 5 (16) ◽  
pp. 162591
Author(s):  
Shweta Aladakatti ◽  
Shubham Singh ◽  
Jasdeep Jain
Keyword(s):  
Low Power ◽  
Optimization Techniques ◽  
Battery Life ◽  
Ultra Low Power

scholarly journals A Low Power IoT-Connected Smart Canister System Creating Automatic Shopping List

2019 ◽  
Vol 8 (3) ◽  
pp. 38 ◽  
Author(s):  
Tareq Khan
Keyword(s):  
Internet Of Things ◽  
Low Power ◽  
Automatic Generation ◽  
Low Energy ◽  
Smartphone App ◽  
The Internet ◽  
Battery Life ◽  
Ultra Low Power ◽  
Peace Of Mind ◽  
Shopping List

One of the most common forgotten things of adults is that they go to the shops and completely forget what they went for. The solution to this problem is to carry a shopping list. In this project, a novel Internet of Things (IoT)-connected smart canister system is developed, which automatically senses the item quantity in the canisters using proximity sensor, sends the data to a hub using Bluetooth Low Energy, and then the hub sends a cloud message to the consumer’s smartphone app using the Internet. The hub and the smartphone app display the item quantities and automatically add the items that are about to finish to a digital shopping list. The automatic generation of the shopping list removes the burden of manually checking each item before going to the shops and gives peace of mind to the consumers. A prototype of the proposed system with three canister devices, one hub, and the smartphone app is developed and tested successfully. The canister device consumes ultra-low power and has a battery life of more than a year.

scholarly journals Single-Tube and Multi-Turn Coil Near-Field Wireless Power Transfer for Low-Power Home Appliances

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1969 ◽  
Author(s):  
Aqeel Jawad ◽  
Rosdiadee Nordin ◽  
Sadik Gharghan ◽  
Haider Jawad ◽  
Mahamod Ismail ◽  
...  
Keyword(s):  
Low Power ◽  
Power Efficiency ◽  
Copper Wire ◽  
Near Field ◽  
Wireless Power Transfer ◽  
Battery Life ◽  
Total System ◽  
Power Transfer ◽  
Wireless Power ◽  
Single Tube

Single-tube loop coil (STLC) and multi-turn copper wire coil (MTCWC) wireless power transfer (WPT) methods are proposed in this study to overcome the challenges of battery life during low-power home appliance operations. Transfer power, efficiency, and distance are investigated for charging mobile devices on the basis of the two proposed systems. The transfer distances of 1–15 cm are considered because the practicality of this range has been proven to be reliable in the current work on mobile device battery charging. For STLC, the Li-ion battery is charged with total system efficiencies of 86.45%, 77.08%, and 52.08%, without a load, at distances of 2, 6, and 15 cm, respectively. When the system is loaded with 100 Ω at the corresponding distances, the transfer efficiencies are reduced to 80.66%, 66.66%, and 47.04%. For MTCWC, the battery is charged with total system efficiencies of 88.54%, 75%, and 52.08%, without a load, at the same distances of 2, 6, and 15 cm. When the system is loaded with 100 Ω at the corresponding distances, the transfer efficiencies are drastically reduced to 39.52%, 33.6%, and 15.13%. The contrasting results, between the STLC and MTCWC methods, are produced because of the misalignment between their transmitters and receiver coils. In addition, the diameter of the MTCWC is smaller than that of the STLC. The output power of the proposed system can charge the latest smartphone in the market, with generated output powers of 5 W (STLC) and 2 W (MTCWC). The above WPT methods are compared with other WPT methods in the literature.

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