Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%

5G Technology in Smart Healthcare and Smart City Development Integration with Deep Learning Architectures

As more and more medical devices, including as mobile phones, sensors, and remote monitoring equipment, require Internet access, wireless networks have gained considerable traction in the healthcare sector. High-performance technologies, such as the forthcoming fifth generation/sixth generation (5G/6G), are needed for data transit to and from medical equipment in order to give patients with state-of-the-art medical treatments. Furthermore, much better optimization techniques must be used when creating its primary components. Intelligent system design affects how all medical equipment operates, which presents a challenging issue in medical applications. Using information from many sources, electronic health records are built and stored there. These data are compiled in several formats and techniques. There are various big data strategies that could be utilised to reconcile the conflicting data. Artificial intelligence, machine learning and deep learning methods can be used to forecast diseases or other problems using the knowledge gathered from big data analytics. With the advent of 5G, augmented reality, virtual reality and spatial computing are all enhanced, which has a profound effect on healthcare informatics by allowing for real-time remote monitoring. With the advent of 5G technologies, healthcare services can be provided over vast distances via a vast network of interconnected devices and high-performance computation. Disease detection and treatment using dynamic data can be accomplished with the help of deep learning techniques such as Deep Convolutional Neural Networks (DCNN). Deep convolutional neural networks that incorporate images of sick regions are frequently employed for classification tasks

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Role of the internet of medical things in care for patients with interstitial lung disease

PURPOSE OF REVIEW: Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review, we will give an overview of different applications of the internet of medical things (IoMT) for patients with ILD. RECENT FINDINGS: Various applications of the IoMT, including teleconsultations, virtual MDTs, digital information, and online peer support, are now used in daily care of patients with ILD. Several studies showed that other IoMT applications, such as online home monitoring and telerehabilitation, seem feasible and reliable, but widespread implementation in clinical practice is lacking. The use of artificial intelligence algorithms and online data clouds in ILD is still in its infancy, but has the potential to improve remote, outpatient clinic, and in-hospital care processes. Further studies in large real-world cohorts to confirm and clinically validate results from previous studies are needed. SUMMARY: We believe that in the near future innovative technologies, facilitated by the IoMT, will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.</p

Internet of Things brings Revolution in eHealth: Achievements and Challenges

The medical field has benefited greatly from the technological revolution around our world, as well as the introduction of artificial intelligence (AI) and the Internet of Things (IoT). IoT aims to make life easier and more convenient by bridging the various gaps in connecting various devices that people employ. A wide range of applications and technologies, including wearable device development, advanced care services, personalized care packages, and remote patient monitoring, benefit healthcare professionals and patients. These technologies gave rise to new terms such as the Internet of Medical Things (IoMT), the Internet of Health Things (IoHT), e-Health, and telemedicine. With the advent of technology and the availability of various connected devices, smart healthcare, which has grown in popularity in recent years, has been positively redefined. Through the selection of literature reviews, we systematically investigate how the adoption (and integration) of IoT technologies in healthcare is changing the way traditional services and products are delivered. This paper outlines (i) selected IoT technologies and paradigms related to health care, as well as, (ii) various implementation scenarios for IoT-based models. It also discusses (iii) the various advantages of these applications and finally, (iv) a summary of lessons learned and recommendations for future applications

A (digital) finger on the pulse

Complex Event Processing (CEP) is a computer-based technique used to track, analyse and process data in real-time (as an event happens). It establishes correlations between streams of information and matches to defined behaviour

Extending remote patient monitoring with mobile real time clinical decision support

Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming\ud biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment