Availability measure model for Assistive Care Loop Framework using wireless sensor networks
- Authors: Balasubramanian, Venki , Hoang, Doan
- Date: 2010
- Type: Text , Conference proceedings
- Relation: 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010,Brisbane, 7th-10th Dec, 2010 published in Proceedings of ISSNIP 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, p. 281-286
- Full Text: false
- Reviewed:
- Description: Nowadays, body area wireless sensor networks (BAWSNs) applications are increasingly being used in in-house health monitoring systems. These applications have stringent timing requirements and often run continuously without interruptions. Hence, it becomes imperative to determine the operational continuity of the BAWSN applications by measuring their availability. The BAWSN applications rely on the collection of data within a critical time from all of the source sensor nodes rather than the data from an individual source. Subsequently, the measure of availability for a BAWSN application should be based on the time and the data delivery from all the sensor nodes. Taking into account these specific characteristics and the constraints of the BAWSN, we develop a model to measure the availability of a BAWSN application based on the unavailable time. The proposed model is evaluated through a series of experiments conducted in our existing Assistive Care Loop Framework (ACLF). Furthermore, we also develop an analogous theoretical model to evaluate the availability of a BAWSN application
A patient agent to manage blockchains for remote patient monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
- Full Text: false
- Reviewed:
- Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
- Description: Studies in Health Technology and Informatics
A scalable cloud Platform for Active healthcare monitoring applications
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Type: Text , Conference paper
- Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.
A genetic algorithm-neural network wrapper approach for bundle branch block detection
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki
- Date: 2016
- Type: Text , Conference paper
- Relation: Computing in Cardiology Conference (CinC), 2016; Vancouver, BC ;11-14 Sept. 2016, published in Computing in Cardiology p. 461-464
- Full Text: false
- Reviewed:
- Description: An Electrocardiogram (ECG) records the electrical impulses of the heart and indicates rhythm anomalies for diagnostic purposes [1], [2]. A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment to reduce morbidity and mortality.
Addressing the confidentiality and integrity of assistive care loop framework using wireless sensor networks
- Authors: Balasubramanian, Venki , Hoang, Doan , Zia, Tanveer
- Date: 2011
- Type: Text , Conference proceedings
- Relation: 21st International Conference on Systems Engineering, ICSEng 2011; Las Vegas, NV; United States; 16th-18th Aug, published in Proceedings - ICSEng 2011: International Conference on Systems Engineering; p. 416-421
- Full Text: false
- Reviewed:
- Description: In-house healthcare monitoring applications are continuous time-critical applications often built upon Body Area Wireless Sensor Networks (BAWSNs). Our Assistive Care Loop Framework (ACLF) is an in-house healthcare application capable of monitoring the health conditions of aged/patients over a dedicated period of time by deploying the BAWSN as the monitoring component. However, the wireless medium used in the BAWSN for communications is prone to vulnerabilities that could open a door to attackers tampering with or compromising the user's data privacy. Hence, it is imperative to maintain the privacy and integrity of the data to gain the confidence and hence, the acceptance of the users of the healthcare applications. Furthermore, in time-critical applications, the vital health conditions must be monitored at regular intervals within their specified critical time. Therefore, the security model proposed for the BAWSN must not incur undue overheads when meeting the critical time requirements of the application. In this paper, we propose and implement a secure adaptive triple-key scheme (aTKS) for the BAWSN to achieve the privacy and integrity of the monitored data with minimal overheads. We then present the performance results of our scheme for the BAWSN, using real-time test-bed implementations and simulations. © 2011 IEEE.
- Description: Proceedings - ICSEng 2011: International Conference on Systems Engineering
A biometric based authentication and encryption Framework for Sensor Health Data in Cloud
- Authors: Sharma, Surender , Balasubramanian, Venki
- Date: 2014
- Type: Text , Conference proceedings
- Full Text:
- Description: Use of remote healthcare monitoring application (HMA) can not only enable healthcare seeker to live a normal life while receiving treatment but also prevent critical healthcare situation through early intervention. For this to happen, the HMA have to provide continuous monitoring through sensors attached to the patient's body or in close proximity to the patient. Owing to elasticity nature of the cloud, recently, the implementation of HMA in cloud is of intense research. Although, cloud-based implementation provides scalability for implementation, the health data of patient is super-sensitive and requires high level of privacy and security for cloud-based shared storage. In addition, protection of real-time arrival of large volume of sensor data from continuous monitoring of patient poses bigger challenge. In this work, we propose a self-protective security framework for our cloud-based HMA. Our framework enable the sensor data in the cloud from (1) unauthorized access and (2) self-protect the data in case of breached access using biometrics. The framework is detailed in the paper using mathematical formulation and algorithms. © 2014 IEEE.
Continuous patient monitoring with a patient centric agent : A block architecture
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
Critical time parameters for evaluation of body area wireless sensor networks in a healthcare monitoring application
- Authors: Balasubramanian, Venki
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: In recent years, the drive for the Healthcare Monitoring Application (HMA) aims to provide continuous remote monitoring of a patient's health. For this to happen, the sensors in the monitoring component of the Body Area Wireless Sensor Networks (BAWSN) need to continuously send data to a Healthcare Application. We show that to provide continuous health data, the BAWSN depends on the collective data delivered by all the sensor nodes and not on a single sensor because medical diagnosis is rarely performed from a single data point. In addition, the arrival time of data should occur within the expected time to be indicative of the actual health of the patient. In this paper, we characterize the HMA as a time-critical application because the BAWSN has stringent timing requirements concerning the arrival of data from the sensor nodes within the defined critical time. Thereby, we formulate the critical time parameters to evaluate the BAWSN operations.
ECG reduction for wearable sensor
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
- Full Text:
- Reviewed:
- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.
A count data model for heart rate variability forecasting and premature ventricular contraction detection
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Journal article
- Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
- Full Text:
- Reviewed:
- Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.
SOAP based assistive care loop using wireless sensor networks
- Authors: Balasubramanian, Venki , Hoang, Doan , Ahmad, N. F.
- Date: 2008
- Type: Text , Conference proceedings
- Relation: 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008; Xiamen; China; 12th-14th December 2008 published in Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008 p. 409-414
- Full Text: false
- Reviewed:
- Description: There is a growing trend towards in-house health monitoring system. It is now feasible to place a Personal Digital Assistant (PDA) or smart phone in the hands of care-delivery staff and the patients regardless of where they are located or what their duties might be. In such instance, the staff would be able to access records and communicate with patients in a flexible and cost effective way. This paper proposes an Active Care Loop Framework (ACLF). The strength of our ACLF is to monitor disease over longer period of time and to consult patient who are then able to discuss their conditions with the care staff. Where patients need emergency intervention, an assistive health monitoring system can provide a direct communication channel to summon assistance and to enable with managing the situation until the assistance arrives. With a regular schedule of monitoring and consultation, the assistive ACLF has the capacity to forestall and manage non-critical situations and therefore the system can be deployed to minimize the rate and costs of hospitalizations. © 2008 Crown.
- Description: Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, ITME 2008
AppA : Assistive patient monitoring cloud platform for active healthcare applications
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a realtime- like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on the monitoring requirements from the health care providers, and is aligned with scalable economic models.
A low-complexity equalizer for video broadcasting in cyber-physical social systems through handheld mobile devices
- Authors: Solyman, Ahmad , Attar, Hani , Khosravi, Mohammad , Menon, Varun , Jolfaei, Alireza , Balasubramanian, Venki , Selvaraj, Buvana , Tavallali, Pooya
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 67591-67602
- Full Text:
- Reviewed:
- Description: In Digital Video Broadcasting-Handheld (DVB-H) devices for cyber-physical social systems, the Discrete Fractional Fourier Transform-Orthogonal Chirp Division Multiplexing (DFrFT-OCDM) has been suggested to enhance the performance over Orthogonal Frequency Division Multiplexing (OFDM) systems under time and frequency-selective fading channels. In this case, the need for equalizers like the Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF) arises, though it is excessively complex due to the need for a matrix inversion, especially for DVB-H extensive symbol lengths. In this work, a low complexity equalizer, Least-Squares Minimal Residual (LSMR) algorithm, is used to solve the matrix inversion iteratively. The paper proposes the LSMR algorithm for linear and nonlinear equalizers with the simulation results, which indicate that the proposed equalizer has significant performance and reduced complexity over the classical MMSE equalizer and other low complexity equalizers, in time and frequency-selective fading channels. © 2013 IEEE.
Performance evaluation of the dependable properties of a body area wireless sensor network
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
- Full Text:
- Reviewed:
- Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.
Developing an interactive electronic maternity record
- Authors: Homer, Caroline , Catling-Paull, Christine , Sinclair, Dee , Faizah, Nor , Balasubramanian, Venki , Foureur, Maralyn , Hoang, Doan , Lawrence, Elaine
- Date: 2010
- Type: Text , Journal article
- Relation: British Journal of Midwifery Vol. 18, no. 6 (2010), p. 384-389
- Full Text: false
- Reviewed:
- Description: Women have a strong need to be involved in their own maternity care. Pregnancy hand-held records encourage women's participation in their maternity care; gives them an increased sense of control and improves communication among care providers. They have been successfully used in the UK and New Zealand for almost 20 years. Despite evidence that supports the use of hand-held records, widespread introduction has not occurred in Australia. The need for an electronic version of pregnancy hand-held records has become apparent, especially after the introduction of the Electronic Medical Record in Australia. A personal digital assistant (PDA) was developed as an interactive antenatal electronic maternity record that health-care providers could use in any setting and women could access using the internet. This article will describe the testing of the antenatal electronic maternity record.
Rapid health data repository allocation using predictive machine learning
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
- Reviewed:
- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
Blockchain leveraged decentralized IoT eHealth framework
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Internet of Things Vol. 9, no. March 2020 p. 100159
- Full Text:
- Reviewed:
- Description: Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Full Text:
- Reviewed:
- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
- Full Text:
- Reviewed:
- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
Atrial fibrillation analysis for real time patient monitoring
- Authors: Allami, Ragheed , Stranieri, Andrew , Marzbanrad, Faezeh , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 44th Computing in Cardiology Conference, CinC 2017 Vol. 44, p. 1-4
- Full Text: false
- Reviewed:
- Description: Atrial Fibrillation (AF) can lead to life-threatening conditions such as stroke and heart failure. The instant recognition of life-threatening cardiac arrhythmias based on a 3-lead ECG to record a Lead II configuration for a few seconds is a challenging problem of clinical significance. Five consecutive ECG beats that were identified by a cardiologist to characterise an AF episode and five consecutive heartbeat intervals representing an irregular RR intervals episode were analysed. The detection and analysis of P waves as the morphological features of AF was executed based on two template matching methods. An AF detector was developed by combining the correlation coefficients based on the template matching methods and the standard deviation of the RR intervals. The AF detector was then applied to classify 5 consecutive beats as AF or non-AF based on thresholding the calculated irregularity. The proposed algorithm was tested on the MIT-BIH Atrial Fibrillation and the Challenge 2017 databases. The proposed method resulted in an improved sensitivity, specificity and accuracy of 97.60%, 98.20% and 99% respectively compared to recent published methods. In addition, the proposed method is suitable for real-time patient monitoring as it is computationally simple and requires only a few seconds of ECG recording to detect an AF rhythm. © 2017 IEEE Computer Society. All rights reserved.