A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis

[EN] The Internet of Things (IoT) technology has been widely introduced to the existing medical system. An eHealth system based on IoT devices has gained widespread popularity. In this article, we propose an IoT eHealth framework to provide an autonomous solution for patients with interventional cardiovascular diseases. In this framework, wearable sensors are used to collect a patient's health data, which is daily monitored by a remote doctor. When the monitoring data is abnormal, the remote doctor will ask for image acquisition of the patient's cardiovascular internal conditions. We leverage edge computing to classify these training images by the local base classifier; thereafter, pseudo-labels are generated according to its output. Moreover, a deep segmentation network is leveraged for the segmentation of stent structs in intravascular optical coherence tomography and intravenous ultrasound images of patients. The experimental results demonstrate that remote and local doctors perform real-time visual communication to complete telesurgery. In the experiments, we adopt the U-net backbone with a pretrained SeResNet34 as the encoder to segment the stent structs. Meanwhile, a series of comparative experiments have been conducted to demonstrate the effectiveness of our method based on accuracy, sensitivity, Jaccard, and dice.This work was supported by the National Key Research and Development Program of China (Grant no. 2020YFB1313703), the National Natural Science Foundation of China (Grant no. 62002304), and the Natural Science Foundation of Fujian Province of China (Grant no. 2020J05002).Huang, C.; Zong, Y.; Chen, J.; Liu, W.; Lloret, J.; Mukherjee, M. (2021). A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis. IEEE Wireless Communications. 28(3):36-43. https://doi.org/10.1109/MWC.001.2000407S364328

Towards activity recommendation from lifelogs

With the increasing availability of passive, wearable sensor devices, digital lifelogs can now be captured for individuals. Lifelogs contain a digital trace of a person’s life, and are characterised by large quantities of rich contextual data. In this paper, we propose a content-based recommender sys- tem to leverage such lifelogs to suggest activities to users. We model lifelogs as timelines of chronological sequences of activity objects, and describe a recommendation framework in which a two-level distance metric is proposed to measure the similarity between current and past timelines. An ini- tial evaluation of our activity recommender performed using a real-world lifelog dataset demonstrates the utility of our approach

Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors

Skin conductivity (i.e., sweat) forms the basis of many physiology-based emotion and stress detection systems. However, such systems typically do not detect the biomarkers present in sweat, and thus do not take advantage of the biological information in the sweat. Likewise, such systems do not detect the volatile organic components (VOC’s) created under stressful conditions. This work presents a review into the current status of human emotional stress biomarkers and proposes the major potential biomarkers for future wearable sensors in affective systems. Emotional stress has been classified as a major contributor in several social problems, related to crime, health, the economy, and indeed quality of life. While blood cortisol tests, electroencephalography and physiological parameter methods are the gold standards for measuring stress; however, they are typically invasive or inconvenient and not suitable for wearable real-time stress monitoring. Alternatively, cortisol in biofluids and VOCs emitted from the skin appear to be practical and useful markers for sensors to detect emotional stress events. This work has identified antistress hormones and cortisol metabolites as the primary stress biomarkers that can be used in future sensors for wearable affective systems

5G Vertical Use Cases and Trials of Transportation

The 5G networks are still being deployed in many countries creating new business opportunities. 5G networks allow us to include new features and deliver new levels of system capacity and efficiency such as higher speed connectivity, ultra low latency connectivity, improved security, distributed networks, virtualizednetworks and so on. They enable us to have new use cases and scenarios such as automated vehicles, smart city, eHealth, and so on. In this paper, 5G vertical use cases and large scale trials of transportation undertaken at the EU 5G-HEART project trial sites across Europe are introduced. Four representative transport use cases are validated in the 5G-HEART project. They are as follows: (1) Platooning that drives a group of vehicles together, (2) Autonomous driving that avoids collision and achieves safer driving and better traffic efficiency, (3) Remote driving support that allows an user or cloud software to control vehicles remotely, and (4) Vehicle data services that provides us with a better vehicle services byinterconnecting various third-party data to autonomous vehicles using5G networks. User requirements and KPIs are analyzed for 5G transportation use cases. The selected results of 5G-HEART transportation vertical trials are presented

Highly-Sensitive Textile Pressure Sensors Enabled by Suspended-Type All Carbon Nanotube Fiber Transistor Architecture.

Among various wearable health-monitoring electronics, electronic textiles (e-textiles) have been considered as an appropriate alternative for a convenient self-diagnosis approach. However, for the realization of the wearable e-textiles capable of detecting subtle human physiological signals, the low-sensing performances still remain as a challenge. In this study, a fiber transistor-type ultra-sensitive pressure sensor (FTPS) with a new architecture that is thread-like suspended dry-spun carbon nanotube (CNT) fiber source (S)/drain (D) electrodes is proposed as the first proof of concept for the detection of very low-pressure stimuli. As a result, the pressure sensor shows an ultra-high sensitivity of ~3050 Pa-1 and a response/recovery time of 258/114 ms in the very low-pressure range of <300 Pa as the fiber transistor was operated in the linear region (VDS = -0.1 V). Also, it was observed that the pressure-sensing characteristics are highly dependent on the contact pressure between the top CNT fiber S/D electrodes and the single-walled carbon nanotubes (SWCNTs) channel layer due to the air-gap made by the suspended S/D electrode fibers on the channel layers of fiber transistors. Furthermore, due to their remarkable sensitivity in the low-pressure range, an acoustic wave that has a very tiny pressure could be detected using the FTPS

A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended