An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions

An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions

The prevalence of mixed genotype infections in Turkish patients with hepatitis c: a multicentered assessment

AKTAS, OSMAN/0000-0002-7762-4108; Karsligil, Tekin/0000-0001-7672-3625; cekin, yesim/0000-0003-4393-5618; Sayiner, Ayca/0000-0001-6750-2353; Altindis, Mustafa/0000-0003-0411-9669WOS: 000463988900008PubMed: 30969089Background: HCV virus infections are one of the major health problems in the world that can cause cirrhosis and liver cancer at a higher rate than other hepatitis data. The aim of this study was to determine the prevalence of mixed infections with different HCV genotypes in Turkey and also to evaluate the current HCV genot pe and subtype distributions by a multicentered assessment. Methods: The HCV genotype data of 17,578 hepatitis C patients collected from 23 centers from different geographic regions covering all Turkey were collected. The data included information about the HCV genotypes in the last 10 years (bail een 2007 and 2016), demographic properties of the patients and the methods/systems used to determine the genotypes. Results: Two hundred twenty-eight of the patients (1.3%) had mixed genotype. The most common mixed genotype combination was 1b + 4 (0.83%) followed by 1a + 1b (0.26%). Genotype distribution varies according to geographical regions. However, genotype 1 (82.92%) was the most common genotype in all regions and all years. This was followed by genotype 3 (7.07%) and genotype 4 (5.43%). A variety of methods were used by the centers including sequencing, pyrosequencing, real-time PCR, in-house RFLP, reverse hybridization (LIPA), and hybridization. Conclusions: Infection with mixed HCV genotypes in Turkey is uncommon. Genotype distribution varies according to geographic regions; the most common genotype 1 is encountered all oN er the country, while genotypes 3 and 4 are only in some of the centers. Since there is limited information about mixed HCV infection, further investigations are needed to determine the clinical importance of mixed HCV infection