Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app

Enhanced heart rate prediction model using damped least-squares algorithm

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6 %, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency

An inference system framework for personal sensor devices in mobile health and internet of things networks

Future healthcare directions include individuals being monitored in real-time during day-to-day activity using wearable sensors. This thesis solves a critical requirement, that of intelligently managing when body sensors should alert doctors of changes to a person’s health status, bringing existing research closer to live health monitoring

Systematic predictive analysis of personalized life expectancy using smart devices

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works

DoS/DDoS-MQTT-IoT: A dataset for evaluating intrusions in IoT networks using the MQTT protocol

Adversaries may exploit a range of vulnerabilities in Internet of Things (IoT) environments. These vulnerabilities are typically exploited to carry out attacks, such as denial-of-service (DoS) attacks, either against the IoT devices themselves, or using the devices to perform the attacks. These attacks are often successful due to the nature of the protocols used in the IoT. One popular protocol used for machine-to-machine IoT communications is the Message Queueing Telemetry Protocol (MQTT). Countermeasures for attacks against MQTT include testing defenses with existing datasets. However, there is a lack of real-world test datasets in this area. For this reason, this paper introduces a DoS/DDoS-MQTT-IoT dataset—that contains various DoS/DDoS attack scenarios using MQTT traffic—to help develop and test countermeasures against such attacks. To this end, a physical IoT testbed was constructed and a large volume of IoT data was generated that included standard MQTT traffic as well as 10 DoS scenarios. The usability of the dataset has been evaluated via machine learning

No soldiers left behind: An IoT-based low-power military mobile health system design

© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user\u27s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information

An energy-efficient and secure data inference framework for internet of health things: A pilot study

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices

SmallSat Platform Development for Coast Guard Academy Collaborative Space-Based Research

Collaborations utilizing small spacecraft in near earth orbit between the U. S. Coast Guard Academy (CGA), Naval Research Lab (NRL), the U. S. Naval Academy (USNA), and the Air Force Institute of Technology (AFIT) have initiated scientific and engineering space-based experiments. Sourced opportunities like the VaSpace ThinSat missions have provided a platform for payload, sensor, and experiment development that would have otherwise been resource prohibitive. We have constructed an impedance probe payload for launch in Fall 2020 derived from the existing ‘Space PlasmA Diagnostic suitE’ (SPADE) mission operating from NASA’s International Space Station. Currently both space and laboratory plasmas are investigated with AC impedance measurements using a radio frequency antenna. Plasma electron density data collected from the ThinSat will however use an innovative surface-mounted dipole antenna to gather the required sheath-plasma and plasma resonance information. On that same launch, a compact multispectral ‘Pixel Sensor’ with a 450 nm – 1000 nm spectral range will add to the existing Inertial Motion Unit, Temperature Sensor, Infrared Sensor, and Energetic Particle Detector baselined in previous launches. Our engineering team has begun to design, build, and test a solar panel deployment and de-orbiting mechanism for a CubeSat with the USNA’s Aerospace Engineering Department that utilizes a miniature motor for deployment actuation. For the motor to produce the required torque, a gear ratio of 20:1 is necessary. Impedance probe optimization, de-orbiting mechanism automation, and data collection obstacles, solutions, and procedures will be reported

Properties of Multifunctional Hybrid Carbon Nanotube/Carbon Fiber Polymer Matrix Composites

For aircraft primary structures, carbon fiber reinforced polymer (CFRP) composites possess many advantages over conventional aluminum alloys due to their light weight, higher strength- and stiffness-to-weight ratios, and low life-cycle maintenance costs. However, the relatively low electrical and thermal conductivities of CFRP composites fail to provide structural safety in certain operational conditions such as lightning strikes. Carbon nanotubes (CNT) offer the potential to enhance the multi-functionality of composites with improved thermal and electrical conductivity. In this study, hybrid CNT/carbon fiber (CF) polymer composites were fabricated by interleaving layers of CNT sheets with Hexcel IM7/8852 prepreg. Resin concentrations from 1 wt% to 50 wt% were used to infuse the CNT sheets prior to composite fabrication. The interlaminar properties of the resulting hybrid composites were characterized by mode I and II fracture toughness testing. Fractographical analysis was performed to study the effect of resin concentration. In addition, multi-directional physical properties like thermal conductivity of the orthotropic hybrid polymer composite were evaluated

Whole exome and targeted deep sequencing identify genome-wide allelic loss and frequent SETDB1 mutations in malignant pleural mesotheliomas.

Malignant pleural mesothelioma (MPM), a rare malignancy with a poor prognosis, is mainly caused by exposure to asbestos or other organic fibers, but the underlying genetic mechanism is not fully understood. Genetic alterations and causes for multiple primary cancer development including MPM are unknown. We used whole exome sequencing to identify somatic mutations in a patient with MPM and two additional primary cancers who had no evidence of venous, arterial, lymphovascular, or perineural invasion indicating dissemination of a primary lung cancer to the pleura. We found that the MPM had R282W, a key TP53 mutation, and genome-wide allelic loss or loss of heterozygosity, a distinct genomic alteration not previously described in MPM. We identified frequent inactivating SETDB1 mutations in this patient and in 68 additional MPM patients (mutation frequency: 10%, 7/69) by targeted deep sequencing. Our observations suggest the possibility of a new genetic mechanism in the development of either MPM or multiple primary cancers. The frequent SETDB1 inactivating mutations suggest there could be new diagnostic or therapeutic options for MPM