Arman Anzanpour

Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things

Current developments in ICTs such as in Internet-of-Things (IoT) and CyberPhysical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e.,gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our system on addressing a medical case study. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.

On the Feasibility of Attribute-Based Encryption on Internet of Things Devices

The Internet of Things (IoT) is emerging with the pace of technology evolution, connecting people and things through the Internet. IoT devices enable large-scale data collection and sharing for a wide range of applications. However, it is challenging to securely manage interconnected IoT devices because the collected data could contain sensitive personal information. The authors believe that attribute-based encryption (ABE) could be an effective cryptographic tool for secure management of IoT devices. However, little research has addressed ABEs actual feasibility in the IoT thus far. This article investigates such feasibility considering well-known IoT platforms--specifically, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. A thorough evaluation confirms that adopting ABE in the IoT is indeed feasible.

Self-aware Early Warning Score System for IoT-Based Personalized Healthcare

Early Warning Score (EWS) system is specified to detect and predict patient deterioration in hospitals. This is achievable via monitoring patient’s vital signs continuously and is often manually done with paper and pen. However, because of the constraints in healthcare resources and the high hospital costs, the patient might not be hospitalized for the whole period of the treatments, which has lead to a demand for in-home or portable EWS systems. Such a personalized EWS system needs to monitor the patient at anytime and anywhere even when the patient is carrying out daily activities. In this paper, we propose a self-aware EWS system which is the reinforced version of the existing EWS systems by using the Internet of Things technologies and the self-awareness concept. Our self-aware approach provides (i) system adaptivity with respect to various situations and (ii) system personalization by paying attention to critical parameters. We evaluate the proposed EWS system using a full system demonstration.

Context-aware early warning system for in-home healthcare using internet-of-things

Early warning score (EWS) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing EWS systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.

Leveraging Fog Computing for Healthcare IoT

Developments in technology have shifted the focus of medical practice from treating a disease to prevention. Currently, a significant enhancement in healthcare is expected to be achieved through the Internet of Things (IoT). There are various wearable IoT devices that track physiological signs and signals in the market already. These devices usually connect to the Internet directly or through a local smart phone or a gateway. Home-based and in hospital patients can be continuously monitored with wearable and implantable sensors and actuators. In most cases, these sensors and actuators are resource constrained to perform computing and operate for longer periods. The use of traditional gateways to connect to the Internet provides only connectivity and limited network services. With the introduction of the Fog computing layer, closer to the sensor network, data analytics and adaptive services can be realized in remote healthcare monitoring. This chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, connecting a network of such gateways, both in home and in hospital use. To show the application of the services, simple healthcare scenarios are presented. The features of the gateway in our Fog implementation are discussed and evaluated.

HiCH: Hierarchical Fog-Assisted Computing Architecture for Healthcare IoT

The Internet of Things (IoT) paradigm holds significant promises for remote health monitoring systems. Due to their life- or mission-critical nature, these systems need to provide a high level of availability and accuracy. On the one hand, centralized cloud-based IoT systems lack reliability, punctuality and availability (e.g., in case of slow or unreliable Internet connection), and on the other hand, fully outsourcing data analytics to the edge of the network can result in diminished level of accuracy and adaptability due to the limited computational capacity in edge nodes. In this paper, we tackle these issues by proposing a hierarchical computing architecture, HiCH, for IoT-based health monitoring systems. The core components of the proposed system are 1) a novel computing architecture suitable for hierarchical partitioning and execution of machine learning based data analytics, 2) a closed-loop management technique capable of autonomous system adjustments with respect to patient’s condition. HiCH benefits from the features offered by both fog and cloud computing and introduces a tailored management methodology for healthcare IoT systems. We demonstrate the efficacy of HiCH via a comprehensive performance assessment and evaluation on a continuous remote health monitoring case study focusing on arrhythmia detection for patients suffering from CardioVascular Diseases (CVDs).

IoT-based remote facial expression monitoring system with sEMG signal

Biopotentials including Electrocardiography (ECG), Electromyography (EMG) and Electroencephalography (EEG) measure the activity of heart, muscles and brain, respectively. They can be used for noninvasive diagnostic applications, assistance in rehabilitation medicine and human-computer interaction. The concept of Internet of Things (IoT) can bring added value to applications with biopotential signals in healthcare and human-computer interaction by integrating multiple technologies such as sensors, wireless communication and data science. In this work, we present a wireless biopotentials remote monitoring and processing system. A prototype with the case study of facial expression recognition using four channel facial sEMG signals is implemented. A multivariate Gaussian classifier is trained off-line from one persons surface EMG (sEMG) signals with four facial expressions: neutral, smile, frown and wrinkle nose. The presented IoT application system is implemented on the basis of an eight channel biopotential measurement device, Wi-Fi module as well as signal processing and classification provided as a Cloud service. In the system, the real-time sEMG data stream is filtered, feature extracted and classified within each data segment and the processed data is visualized in a browser remotely together with the classification result.

Self-awareness in remote health monitoring systems using wearable electronics

In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control. This work presents a remote monitoring and diagnostic system that provides a holistic perspective of patients and their health conditions. We discuss how the concept of self-awareness can be used in various parts of the system such as information collection through wearable sensors, confidence assessment of the sensory data, the knowledge base of the patients health situation, and automation of reasoning about the health situation. Our approach to self-awareness provides (i) situation awareness to consider the impact of variations such as sleeping, walking, running, and resting, (ii) system personalization by reflecting parameters such as age, body mass index, and gender, and (iii) the attention property of self-awareness to improve the energy efficiency and dependability of the system via adjusting the priorities of the sensory data collection. We evaluate the proposed method using a full system demonstration.

Portable multipurpose bio-signal acquisition and wireless streaming device for wearables

Physical indicators are directly related with health and fitness of human body. By employing real-time e-health monitoring systems for acquiring, and analyzing bio-signals by measurements such as electrocardiogram (ECG) and electromyography (EMG), it is possible to extract information to achieve better health-care in terms of observation, diagnosis, and treatment. However, those systems are limited in acquiring and sending data at high rates, are not energy efficient, or, are restricted in terms of portability due to large size and weight. In this paper, a compact portable bio-signal acquisition device for wearables has been designed and implemented. The developed hardware is capable of acquiring and reliably sending the data wirelessly at a high transfer rate in real-time while keeping the overall energy consumption low. Finally, the signal acquisition performance of the device has been evaluated for both ECG and EMG at 8 channel 24 bit resolution/channel 500 samples/s configuration. Measurement of energy consumption has been conducted using professional tool and it is found that the device can continuously work for up to 13.6 hours with a 3.7V 1700 mAh battery. In addition, the device has been used in an IoT-based system as an example of possible integration.

Medical warning system based on Internet of Things using fog computing

Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current IoT-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new IoT-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in ECG signal.