A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

Muzammal, Muhammad; Talat, Romana; Sodhro, Ali Hassan; Pirbhulal, Sandeep

doi:10.1016/j.inffus.2019.06.021

Cited by 249 publications

(104 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous works [22]- [24] attempted to utilize the gyroscope and accelerometer as separate wearable devices. However, using these miniaturized wearable devices raises concerns regarding battery life and signal fusion.…”

Section: B Stroke Detection Using Mobile Devicesmentioning

confidence: 99%

Stroke Screening Feature Selection for Arm Weakness Using a Mobile Application

et al. 2020

View full text Add to dashboard Cite

This work studies the features of a proposed automated stroke self-screening application that utilizes the gyroscope and accelerometer devices in smartphones to determine the possible onset of a stroke by assessing arm muscle weakness. The application requires users to perform two arm movements to evaluate arm weakness and pronation: Curl-up and Raise-up. For the purpose of the study, 68 subjects, consisting of 36 stroke patients with symptoms of arm weakness and 32 healthy subjects, consented to participate. A total of 78 handcrafted features were proposed, 26 of which were extracted from Curl-up and Raise-up for each arm. Then, the differences between corresponding features for each arm were calculated. These features were then tested on 63 combinations of three classical feature selection methods, three feature sets (i.e., Curl-up-only features, Raise-up-only features, and both-exercises combined features) and seven well-known classification methods. The results from ten runs of 10-fold cross-validation showed that Curlup-only features achieved an average sensitivity of 83.3%, significantly higher than those of the Raise-uponly features or both-exercises features. From all possible combinations, the random forest classification based on information gain feature selection from Curl-up-only features achieved the most efficient results for arm-weakness-stroke screening. It achieved an average sensitivity of 94.8%, an average specificity of 75.2%, an average accuracy of 84.1%, and an average area under the receiver operating characteristic curve of 85.0%. Our work proposes a novel accessible method to screen symptoms of arm weakness that may indicate the onset of a stroke using a single mobile device. In the future, we can combine this method with other methods of evaluating facial drooping and slurred speech to create a complete Face, Arm, Speech, Time (FAST) assessment application.

show abstract

Section: B Stroke Detection Using Mobile Devicesmentioning

confidence: 99%

Stroke Screening Feature Selection for Arm Weakness Using a Mobile Application

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Representative work can be observed in [ 11 ] where a complete fusion framework for medical-related data is presented. As the diversity of medical sensors increases, it is mandatory that certain ways of managing different data to be developed, exactly as in [ 11 ], where the effectiveness of the proposed method is also explored.…”

Section: Related Workmentioning

confidence: 99%

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

Svertoka

Bălănescu²,

Suciu³

et al. 2020

Sensors

View full text Add to dashboard Cite

As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution.

show abstract

“…However, due to the sporadic nature of ECG signals, it is necessary to monitor patients continuously to have for accurate analysis of the heart problems [4]. Recently, advancements in Internet of Things (IoT) based medical sensors have grown progressively [5][6][7][8][9][10][11][12][13][14][15]; especially in heartbeat sensors that generate real-time delay-sensitive data that require immediate action for the results [16,17]. Generally, these sensors are integrated with limited constraint devices.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

View full text Add to dashboard Cite

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

show abstract

A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

Cited by 249 publications

References 43 publications

Stroke Screening Feature Selection for Arm Weakness Using a Mobile Application

Stroke Screening Feature Selection for Arm Weakness Using a Mobile Application

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Contact Info

Product

Resources

About