Description:
Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
Description:
Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
Description:
Proceedings of the IEEE International Conference on Industrial Technology
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.
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Studies in Health Technology and Informatics
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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.
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.
Description:
Background: This regional pilot site ‘end-user attitudes’ study explored nurses’ experiences and impressions of using the Nurses’ Behavioural Assistant (NBA) (a knowledge-based, interactive ehealth system) to assist them to better respond to behavioural and psychological symptoms of dementia (BPSD) and will be reported here. Methods: Focus groups were conducted, followed by a four-week pilot site ‘end-user attitudes’ trial of the NBA at a regional aged care residential facility (ACRF). Brief interviews were conducted with consenting nursing staff. Results: Focus group feedback (N = 10) required only minor cosmetic changes to the NBA prototype. Post pilot site end-user interview data (N = 10) indicated that the regional ACRF nurses were positive and enthusiastic about the NBA, however several issues were also identified. Conclusions: Overall the results supported the utility of the NBA to promote a person centred care approach to managing BPSD. Slight modifications may be required to maximise its uptake across all ACRF nursing staff.
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.
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.
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.
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.
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.
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.