No abstract

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.

Attribute-Based Encryption (ABE) could be an effective cryptographic tool for the secure management of Internet-of-Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero, and concludes that adopting ABE in the IoT is indeed feasible.Accepted for publication --IEEE Micro Special Issue on Internet of Things (2016) Preprint versionAttribute-Based Encryption and IoT. In recent years, several security protocols adopted Attribute-Based Encryption (ABE) as a building block in different distributed environments [3], such as IoT [4], cloud services [5], and medical systems [6]. ABE is a public key scheme where both encryption and decryption are based on high-level data access policies. Considering the aforementioned requirements in distributed and heterogeneous IoT scenarios, ABE provides more efficient access control mechanism compared to conventional cryptographic algorithms [3], [6], [7]: (i) allows fine-grained access control based on recipients' attributes; (ii) scales independent from the number of authorized users; (iii) is resilient against collusion attacks; (iv) does not require key sharing or key management algorithms between the participating parties (data owner does not need to identify the destination client). Besides its noteworthy advantages, a proper key revocation algorithm is still a challenging issue in ABE (beyond the scope of this paper), and an ongoing research effort [3]. More relevant to our work, ABE suffers from high computational overhead [6], [8]. However, the literature is still missing a proper assessment of ABE efficiency on resource-constrained devices, widely used in the IoT domain.In order to shine a light on the feasibility of ABE in IoT, we perform a comprehensive analysis of the cost of ABE operations on resource-constrained devices. In particular, along the same line of our previous work [7], which investigated the feasibility of ABE on smartphone devices, in this paper we implement the original Key-Policy Attribute-Based Encryption (KP-ABE) [9] and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) [10] on widely used IoT-enabling devices. Our work focuses on the evaluation of encryption and decryption (hereinafter called cryptographic operations ) on four boards: Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. Due to space limitation, we only report the results for CP-ABE. However, we noticed that the KP-ABE experiments have a very similar quantitative behavior to CP-ABE results. Supported by our observations from thorough experimental results, we provide evidence of the feasibility of adopting ABE on resource-constrained devices. Moreover, we present a smart healthcare use case application to evaluate feasibility of using ABE in real world IoT scenarios.

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.