Continuous glucose monitoring sensors: Past, present and future algorithmic challenges

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

Study of optical techniques for the Ames unitary wind tunnel: Digital image processing, part 6

A survey of digital image processing techniques and processing systems for aerodynamic images has been conducted. These images covered many types of flows and were generated by many types of flow diagnostics. These include laser vapor screens, infrared cameras, laser holographic interferometry, Schlieren, and luminescent paints. Some general digital image processing systems, imaging networks, optical sensors, and image computing chips were briefly reviewed. Possible digital imaging network systems for the Ames Unitary Wind Tunnel were explored

Characterization of the neuroendocrine pancreatic tumors nature by MDCT enhancement pattern: a radio-pathological correlation

Introduction Pre-operative suspicion of neuroendocrine pancreatic lesions nature arises both from clinical (presence and the type of secreted hormone) and imaging findings. However, imaging suggestion of lesion nature is based quite only on nodular dimension and on the presence of local and distant spreading. Aim of the study was to determine the nature of neuroendocrine pancreatic lesions by analysing lesions enhancement pattern at MDCT and by comparing it with histological findings, including the MVD. Materials and Methods We included 45 patients submitted to surgical resection for pancreatic neuroendocrine tumor. All preoperative CT examinations were performed by a multidetector CT. Post-contrastographic study included 4 phases: early arterial (delay 15-20”), pancreatic (delay 35”), venous (delay 70”) and late phases (delay 180”). Two different patterns of enhancement were defined: pattern A, including lesions showing early enhancement (during early arterial or pancreatic phase) and a rapid wash-out; pattern B, including lesions with wash-in in the early arterial or pancreatic phase with no wash-out nor in the late phase (pattern B1), and lesions showing enhancement only in the venous and/or late phases (pattern B2). Results 66 lesions were detected (30 pattern A, 26 B1 and 10 B2). At pathology 28 lesions were adenomas, 14 borderline and 24 carcinomas: 24/30 lesions showing pattern A were benign, 5 borderline and 1 carcinoma; 23/36 lesions showing pattern B were carcinomas, 9 borderline and 4 adenomas. Among the 26 B1 lesions, 13 were carcinomas, 9 borderline and 4 adenomas, while all 10 B2 lesions were malignant. Pattern A showed PPV of benignancy of 80%, and pattern B NPV of benignancy of 89%. MVD was evaluated in 22 lesions obtaining significant differences among the 3 histological and the 3 enhancement pattern. Significant differences between B1 and B2 malignant lesions existed by considering metastases (only B2 lesions) and fibrosis (all B2 lesions). Conclusion The enhancement pattern at CT is related to MVD and the histological type, thus representing a further criterium for suggesting nature of neuroendocrine lesions. The low MVD of B2 lesions, associated with the presence of fibrosis, may justify the delayed enhancement of these lesions

Design methodology for smart actuator services for machine tool and machining control and monitoring

This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented