Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things.

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network.

Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System.

Electromyogram (EMG) pattern-recognition (PR) is the most widely adopted prostheses/rehabilitation robots control method that seamlessly support multi-degrees of freedom (MDF) function in an intuitive fashion. The feature extraction framework applied in such PR-based control essentially determines the control performance of the prosthetic device. Based on the drawbacks of the commonly utilized feature extraction methods, this study proposed a spatio-temporal-based feature set (STFS) that might optimally characterize EMG signal patterns even in the presence of white Gaussian noise (WGN) to realize consistently accurate and stable decoding of multiple classes of limb-movements.

Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System.

Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological Stress Response Inventory, biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment.

Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks.

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25-30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study.

An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone's life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments.

Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69.

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Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067-15089.

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A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range.

Heart-Carotid Pulse Wave Velocity a Useful Index of Atherosclerosis in Chinese Hypertensive Patients.

This study was designed to investigate the relationship between heart-carotid pulse wave velocity (hcPWV) and carotid intima-media thickness (CIMT) in hypertensive patients, and also to examine the effect of pre-ejection period (PEP) on it. Doppler ultrasound device was used to measure CIMT in left common carotid artery. Hypertensive patients were divided into normal (n = 36, CIMT ≤0.

A novel ultrasound based approach for lesion segmentation and its applications in gynecological laparoscopic surgery.

Laparoscopic ultrasound (LUS) has been widely utilized as a surgical aide in general, urological, and gynecological applications. Our study summarizes the clinical applications of laparoscopic ultrasonography in laparoscopic gynecologic surgery. Retrospective analyses were performed on 42 women subjects using laparoscopic surgery during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013.

Relationship of short-term blood pressure variability with carotid intima-media thickness in hypertensive patients.

Background: High blood pressure (BP) is among significant risk factor for stroke and other vascular occurrences, it experiences nonstop fluctuations over time as a result of a complex interface among cardiovascular control mechanisms. Large blood pressure variability (BPV) has been proved to be promising in providing potential regulatory mechanisms of the cardiovascular system. Although the previous studies also showed that BPV is associated with increased carotid intima-media thickness (IMT) and plaque, whether the correlation between variability in blood pressure and left common carotid artery-intima-media thickness (LCCA-IMT) is stronger than right common carotid artery-intima-media thickness (RCCA-IMT) remains uncertain in hypertension.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission.

Quantification of three-dimensional computed tomography angiography for evaluating coronary luminal stenosis using digital subtraction angiography as the standard of reference.

Objective: We sought to evaluate the accuracy of quantitative three-dimensional (3D) CT angiography (CTA) for the assessment of coronary luminal stenosis using digital subtraction angiography (DSA) as the standard of reference.

Method: Twenty-three patients with 54 lesions were referred for CTA followed by DSA. The CTA scans were performed with 256-slice spiral CT.