This paper reports the development of a new smart ECG monitoring system, consisting of the related hardware, firmware, and IoT-based web service for AI assisted arrhythmia detection and a complementary Android application for data streaming. The hardware aspect of this research proposes an ultra-low power patch sampling ECG data at 256 samples/s with 16-bit resolution. The battery life of the device is two weeks per charging, which alongside the flexible and slim (193.7 mm × 62.4 mm × 8.6 mm) and lightweight (43 g) allows the user to continue real life activities while the real-time monitoring is being done without interruption. The power management is achieved through the usage of switching converters, ultra-low power component choice as well as intermittent usage of them through firmware optimization. A novel data encoding method is also proposed to allow compression of data and lower the runtime.The software aspect, in addition to the web ECG analysis platform and the Android streaming and monitoring application, provides an arrhythmia detection service. The key innovations in this regard are the usage of a set of new factors in determining arrhythmia that grants higher accuracy while retaining the detection near-real-time. 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.

Every year, a large number of the Earth inhabitants have to leave their homeland due to violence and discrimination so that there are now over 60 million displaced people worldwide. Almost 20 million of them are refugees. In this circumstance, refugee camps, considered a common settling way, not only have a great effect on the current lives of refugees, but also influence their future lives because of the long-term presence of refugees in such an environment. The purpose of this study is to review the refugee camps from economic, social, physical, managerial, and environmental perspectives. In the next step, the rational basis of refugee camps as a city is explained by examining city, its features, and its dimensions and comparison with refugee camps. Afterward, diverse criteria are developed to improve the quality of life in various social, environmental and physical dimensions using the livability approach. These criteria are the basis of redesigning Saveh Camp, Iran as a livable city. Finally, a livable city-based design is proposed for the case study.

<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>

<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>

<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>